writing scientific article presentation

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How to make a scientific presentation

Scientific presentation outlines

Questions to ask yourself before you write your talk, 1. how much time do you have, 2. who will you speak to, 3. what do you want the audience to learn from your talk, step 1: outline your presentation, step 2: plan your presentation slides, step 3: make the presentation slides, slide design, text elements, animations and transitions, step 4: practice your presentation, final thoughts, frequently asked questions about preparing scientific presentations, related articles.

A good scientific presentation achieves three things: you communicate the science clearly, your research leaves a lasting impression on your audience, and you enhance your reputation as a scientist.

But, what is the best way to prepare for a scientific presentation? How do you start writing a talk? What details do you include, and what do you leave out?

It’s tempting to launch into making lots of slides. But, starting with the slides can mean you neglect the narrative of your presentation, resulting in an overly detailed, boring talk.

The key to making an engaging scientific presentation is to prepare the narrative of your talk before beginning to construct your presentation slides. Planning your talk will ensure that you tell a clear, compelling scientific story that will engage the audience.

In this guide, you’ll find everything you need to know to make a good oral scientific presentation, including:

• The different types of oral scientific presentations and how they are delivered;
• How to outline a scientific presentation;
• How to make slides for a scientific presentation.

Our advice results from delving into the literature on writing scientific talks and from our own experiences as scientists in giving and listening to presentations. We provide tips and best practices for giving scientific talks in a separate post.

There are two main types of scientific talks:

• Your talk focuses on a single study . Typically, you tell the story of a single scientific paper. This format is common for short talks at contributed sessions in conferences.
• Your talk describes multiple studies. You tell the story of multiple scientific papers. It is crucial to have a theme that unites the studies, for example, an overarching question or problem statement, with each study representing specific but different variations of the same theme. Typically, PhD defenses, invited seminars, lectures, or talks for a prospective employer (i.e., “job talks”) fall into this category.

➡️ Learn how to prepare an excellent thesis defense

The length of time you are allotted for your talk will determine whether you will discuss a single study or multiple studies, and which details to include in your story.

The background and interests of your audience will determine the narrative direction of your talk, and what devices you will use to get their attention. Will you be speaking to people specializing in your field, or will the audience also contain people from disciplines other than your own? To reach non-specialists, you will need to discuss the broader implications of your study outside your field.

The needs of the audience will also determine what technical details you will include, and the language you will use. For example, an undergraduate audience will have different needs than an audience of seasoned academics. Students will require a more comprehensive overview of background information and explanations of jargon but will need less technical methodological details.

Your goal is to speak to the majority. But, make your talk accessible to the least knowledgeable person in the room.

This is called the thesis statement, or simply the “take-home message”. Having listened to your talk, what message do you want the audience to take away from your presentation? Describe the main idea in one or two sentences. You want this theme to be present throughout your presentation. Again, the thesis statement will depend on the audience and the type of talk you are giving.

Your thesis statement will drive the narrative for your talk. By deciding the take-home message you want to convince the audience of as a result of listening to your talk, you decide how the story of your talk will flow and how you will navigate its twists and turns. The thesis statement tells you the results you need to show, which subsequently tells you the methods or studies you need to describe, which decides the angle you take in your introduction.

➡️ Learn how to write a thesis statement

The goal of your talk is that the audience leaves afterward with a clear understanding of the key take-away message of your research. To achieve that goal, you need to tell a coherent, logical story that conveys your thesis statement throughout the presentation. You can tell your story through careful preparation of your talk.

Preparation of a scientific presentation involves three separate stages: outlining the scientific narrative, preparing slides, and practicing your delivery. Making the slides of your talk without first planning what you are going to say is inefficient.

Here, we provide a 4 step guide to writing your scientific presentation:

• Outline your presentation
• Plan your presentation slides
• Make the presentation slides
• Practice your presentation

Writing an outline helps you consider the key pieces of your talk and how they fit together from the beginning, preventing you from forgetting any important details. It also means you avoid changing the order of your slides multiple times, saving you time.

Plan your talk as discrete sections. In the table below, we describe the sections for a single study talk vs. a talk discussing multiple studies:

The following tips apply when writing the outline of a single study talk. You can easily adapt this framework if you are writing a talk discussing multiple studies.

Introduction: Writing the introduction can be the hardest part of writing a talk. And when giving it, it’s the point where you might be at your most nervous. But preparing a good, concise introduction will settle your nerves.

The introduction tells the audience the story of why you studied your topic. A good introduction succinctly achieves four things, in the following order.

• It gives a broad perspective on the problem or topic for people in the audience who may be outside your discipline (i.e., it explains the big-picture problem motivating your study).
• It describes why you did the study, and why the audience should care.
• It gives a brief indication of how your study addressed the problem and provides the necessary background information that the audience needs to understand your work.
• It indicates what the audience will learn from the talk, and prepares them for what will come next.

A good introduction not only gives the big picture and motivations behind your study but also concisely sets the stage for what the audience will learn from the talk (e.g., the questions your work answers, and/or the hypotheses that your work tests). The end of the introduction will lead to a natural transition to the methods.

Give a broad perspective on the problem. The easiest way to start with the big picture is to think of a hook for the first slide of your presentation. A hook is an opening that gets the audience’s attention and gets them interested in your story. In science, this might take the form of a why, or a how question, or it could be a statement about a major problem or open question in your field. Other examples of hooks include quotes, short anecdotes, or interesting statistics.

Why should the audience care? Next, decide on the angle you are going to take on your hook that links to the thesis of your talk. In other words, you need to set the context, i.e., explain why the audience should care. For example, you may introduce an observation from nature, a pattern in experimental data, or a theory that you want to test. The audience must understand your motivations for the study.

Supplementary details. Once you have established the hook and angle, you need to include supplementary details to support them. For example, you might state your hypothesis. Then go into previous work and the current state of knowledge. Include citations of these studies. If you need to introduce some technical methodological details, theory, or jargon, do it here.

Conclude your introduction. The motivation for the work and background information should set the stage for the conclusion of the introduction, where you describe the goals of your study, and any hypotheses or predictions. Let the audience know what they are going to learn.

Methods: The audience will use your description of the methods to assess the approach you took in your study and to decide whether your findings are credible. Tell the story of your methods in chronological order. Use visuals to describe your methods as much as possible. If you have equations, make sure to take the time to explain them. Decide what methods to include and how you will show them. You need enough detail so that your audience will understand what you did and therefore can evaluate your approach, but avoid including superfluous details that do not support your main idea. You want to avoid the common mistake of including too much data, as the audience can read the paper(s) later.

Results: This is the evidence you present for your thesis. The audience will use the results to evaluate the support for your main idea. Choose the most important and interesting results—those that support your thesis. You don’t need to present all the results from your study (indeed, you most likely won’t have time to present them all). Break down complex results into digestible pieces, e.g., comparisons over multiple slides (more tips in the next section).

Summary: Summarize your main findings. Displaying your main findings through visuals can be effective. Emphasize the new contributions to scientific knowledge that your work makes.

Conclusion: Complete the circle by relating your conclusions to the big picture topic in your introduction—and your hook, if possible. It’s important to describe any alternative explanations for your findings. You might also speculate on future directions arising from your research. The slides that comprise your conclusion do not need to state “conclusion”. Rather, the concluding slide title should be a declarative sentence linking back to the big picture problem and your main idea.

It’s important to end well by planning a strong closure to your talk, after which you will thank the audience. Your closing statement should relate to your thesis, perhaps by stating it differently or memorably. Avoid ending awkwardly by memorizing your closing sentence.

By now, you have an outline of the story of your talk, which you can use to plan your slides. Your slides should complement and enhance what you will say. Use the following steps to prepare your slides.

• Write the slide titles to match your talk outline. These should be clear and informative declarative sentences that succinctly give the main idea of the slide (e.g., don’t use “Methods” as a slide title). Have one major idea per slide. In a YouTube talk on designing effective slides , researcher Michael Alley shows examples of instructive slide titles.
• Decide how you will convey the main idea of the slide (e.g., what figures, photographs, equations, statistics, references, or other elements you will need). The body of the slide should support the slide’s main idea.
• Under each slide title, outline what you want to say, in bullet points.

In sum, for each slide, prepare a title that summarizes its major idea, a list of visual elements, and a summary of the points you will make. Ensure each slide connects to your thesis. If it doesn’t, then you don’t need the slide.

Slides for scientific presentations have three major components: text (including labels and legends), graphics, and equations. Here, we give tips on how to present each of these components.

• Have an informative title slide. Include the names of all coauthors and their affiliations. Include an attractive image relating to your study.
• Make the foreground content of your slides “pop” by using an appropriate background. Slides that have white backgrounds with black text work well for small rooms, whereas slides with black backgrounds and white text are suitable for large rooms.
• The layout of your slides should be simple. Pay attention to how and where you lay the visual and text elements on each slide. It’s tempting to cram information, but you need lots of empty space. Retain space at the sides and bottom of your slides.
• Use sans serif fonts with a font size of at least 20 for text, and up to 40 for slide titles. Citations can be in 14 font and should be included at the bottom of the slide.
• Use bold or italics to emphasize words, not underlines or caps. Keep these effects to a minimum.
• Use concise text . You don’t need full sentences. Convey the essence of your message in as few words as possible. Write down what you’d like to say, and then shorten it for the slide. Remove unnecessary filler words.
• Text blocks should be limited to two lines. This will prevent you from crowding too much information on the slide.
• Include names of technical terms in your talk slides, especially if they are not familiar to everyone in the audience.
• Proofread your slides. Typos and grammatical errors are distracting for your audience.
• Include citations for the hypotheses or observations of other scientists.
• Good figures and graphics are essential to sustain audience interest. Use graphics and photographs to show the experiment or study system in action and to explain abstract concepts.
• Don’t use figures straight from your paper as they may be too detailed for your talk, and details like axes may be too small. Make new versions if necessary. Make them large enough to be visible from the back of the room.
• Use graphs to show your results, not tables. Tables are difficult for your audience to digest! If you must present a table, keep it simple.
• Label the axes of graphs and indicate the units. Label important components of graphics and photographs and include captions. Include sources for graphics that are not your own.
• Explain all the elements of a graph. This includes the axes, what the colors and markers mean, and patterns in the data.
• Use colors in figures and text in a meaningful, not random, way. For example, contrasting colors can be effective for pointing out comparisons and/or differences. Don’t use neon colors or pastels.
• Use thick lines in figures, and use color to create contrasts in the figures you present. Don’t use red/green or red/blue combinations, as color-blind audience members can’t distinguish between them.
• Arrows or circles can be effective for drawing attention to key details in graphs and equations. Add some text annotations along with them.
• Write your summary and conclusion slides using graphics, rather than showing a slide with a list of bullet points. Showing some of your results again can be helpful to remind the audience of your message.
• If your talk has equations, take time to explain them. Include text boxes to explain variables and mathematical terms, and put them under each term in the equation.
• Combine equations with a graphic that shows the scientific principle, or include a diagram of the mathematical model.
• Use animations judiciously. They are helpful to reveal complex ideas gradually, for example, if you need to make a comparison or contrast or to build a complicated argument or figure. For lists, reveal one bullet point at a time. New ideas appearing sequentially will help your audience follow your logic.
• Slide transitions should be simple. Silly ones distract from your message.
• Decide how you will make the transition as you move from one section of your talk to the next. For example, if you spend time talking through details, provide a summary afterward, especially in a long talk. Another common tactic is to have a “home slide” that you return to multiple times during the talk that reinforces your main idea or message. In her YouTube talk on designing effective scientific presentations , Stanford biologist Susan McConnell suggests using the approach of home slides to build a cohesive narrative.

To deliver a polished presentation, it is essential to practice it. Here are some tips.

• For your first run-through, practice alone. Pay attention to your narrative. Does your story flow naturally? Do you know how you will start and end? Are there any awkward transitions? Do animations help you tell your story? Do your slides help to convey what you are saying or are they missing components?
• Next, practice in front of your advisor, and/or your peers (e.g., your lab group). Ask someone to time your talk. Take note of their feedback and the questions that they ask you (you might be asked similar questions during your real talk).
• Edit your talk, taking into account the feedback you’ve received. Eliminate superfluous slides that don’t contribute to your takeaway message.
• Practice as many times as needed to memorize the order of your slides and the key transition points of your talk. However, don’t try to learn your talk word for word. Instead, memorize opening and closing statements, and sentences at key junctures in the presentation. Your presentation should resemble a serious but spontaneous conversation with the audience.
• Practicing multiple times also helps you hone the delivery of your talk. While rehearsing, pay attention to your vocal intonations and speed. Make sure to take pauses while you speak, and make eye contact with your imaginary audience.
• Make sure your talk finishes within the allotted time, and remember to leave time for questions. Conferences are particularly strict on run time.
• Anticipate questions and challenges from the audience, and clarify ambiguities within your slides and/or speech in response.
• If you anticipate that you could be asked questions about details but you don’t have time to include them, or they detract from the main message of your talk, you can prepare slides that address these questions and place them after the final slide of your talk.

➡️ More tips for giving scientific presentations

An organized presentation with a clear narrative will help you communicate your ideas effectively, which is essential for engaging your audience and conveying the importance of your work. Taking time to plan and outline your scientific presentation before writing the slides will help you manage your nerves and feel more confident during the presentation, which will improve your overall performance.

A good scientific presentation has an engaging scientific narrative with a memorable take-home message. It has clear, informative slides that enhance what the speaker says. You need to practice your talk many times to ensure you deliver a polished presentation.

First, consider who will attend your presentation, and what you want the audience to learn about your research. Tailor your content to their level of knowledge and interests. Second, create an outline for your presentation, including the key points you want to make and the evidence you will use to support those points. Finally, practice your presentation several times to ensure that it flows smoothly and that you are comfortable with the material.

Prepare an opening that immediately gets the audience’s attention. A common device is a why or a how question, or a statement of a major open problem in your field, but you could also start with a quote, interesting statistic, or case study from your field.

Scientific presentations typically either focus on a single study (e.g., a 15-minute conference presentation) or tell the story of multiple studies (e.g., a PhD defense or 50-minute conference keynote talk). For a single study talk, the structure follows the scientific paper format: Introduction, Methods, Results, Summary, and Conclusion, whereas the format of a talk discussing multiple studies is more complex, but a theme unifies the studies.

Ensure you have one major idea per slide, and convey that idea clearly (through images, equations, statistics, citations, video, etc.). The slide should include a title that summarizes the major point of the slide, should not contain too much text or too many graphics, and color should be used meaningfully.

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Scientific Presentation Guide: How to Create an Engaging Research Talk

Creating an effective scientific presentation requires developing clear talking points and slide designs that highlight your most important research results..

Scientific presentations are detailed talks that showcase a research project or analysis results. This comprehensive guide reviews everything you need to know to give an engaging presentation for scientific conferences, lab meetings, and PhD thesis talks. From creating your presentation outline to designing effective slides, the tips in this article will give you the tools you need to impress your scientific peers and superiors.

Step 1. Create a Presentation Outline

The first step to giving a good scientific talk is to create a presentation outline that engages the audience at the start of the talk, highlights only 3-5 main points of your research, and then ends with a clear take-home message. Creating an outline ensures that the overall talk storyline is clear and will save you time when you start to design your slides.

Engage Your Audience

The first part of your presentation outline should contain slide ideas that will gain your audience's attention. Below are a few recommendations for slides that engage your audience at the start of the talk:

• Create a slide that makes connects your data or presentation information to a shared purpose, such as relevance to solving a medical problem or fundamental question in your field of research
• Create slides that ask and invite questions
• Use humor or entertainment

Identify Clear Main Points

After writing down your engagement ideas, the next step is to list the main points that will become the outline slide for your presentation. A great way to accomplish this is to set a timer for five minutes and write down all of the main points and results or your research that you want to discuss in the talk. When the time is up, review the points and select no more than three to five main points that create your talk outline. Limiting the amount of information you share goes a long way in maintaining audience engagement and understanding. 

Create a Take-Home Message

And finally, you should brainstorm a single take-home message that makes the most important main point stand out. This is the one idea that you want people to remember or to take action on after your talk. This can be your core research discovery or the next steps that will move the project forward.

Step 2. Choose a Professional Slide Theme

After you have a good presentation outline, the next step is to choose your slide colors and create a theme. Good slide themes use between two to four main colors that are accessible to people with color vision deficiencies. Read this article to learn more about choosing the best scientific color palettes .

You can also choose templates that already have an accessible color scheme. However, be aware that many PowerPoint templates that are available online are too cheesy for a scientific audience. Below options to download professional scientific slide templates that are designed specifically for academic conferences, research talks, and graduate thesis defenses.

Step 3. Design Your Slides

Designing good slides is essential to maintaining audience interest during your scientific talk. Follow these four best practices for designing your slides:

• Keep it simple: limit the amount of information you show on each slide
• Use images and illustrations that clearly show the main points with very little text. 
• Read this article to see research slide example designs for inspiration
• When you are using text, try to reduce the scientific jargon that is unnecessary. Text on research talk slides needs to be much more simple than the text used in scientific publications (see example below).
• Use appear/disappear animations to break up the details into smaller digestible bites
• Sign up for the free presentation design course to learn PowerPoint animation tricks

Scientific Presentation Design Summary

All of the examples and tips described in this article will help you create impressive scientific presentations. Below is the summary of how to give an engaging talk that will earn respect from your scientific community. 

Step 1. Draft Presentation Outline. Create a presentation outline that clearly highlights the main point of your research. Make sure to start your talk outline with ideas to engage your audience and end your talk with a clear take-home message.

Step 2. Choose Slide Theme. Use a slide template or theme that looks professional, best represents your data, and matches your audience's expectations. Do not use slides that are too plain or too cheesy.

Step 3. Design Engaging Slides. Effective presentation slide designs use clear data visualizations and limits the amount of information that is added to each slide. 

And a final tip is to practice your presentation so that you can refine your talking points. This way you will also know how long it will take you to cover the most essential information on your slides. Thank you for choosing Simplified Science Publishing as your science communication resource and good luck with your presentations!

Interested in free design templates and training?

Explore scientific illustration templates and courses by creating a Simplified Science Publishing Log In. Whether you are new to data visualization design or have some experience, these resources will improve your ability to use both basic and advanced design tools.

Interested in reading more articles on scientific design? Learn more below:

Data Storytelling Techniques: How to Tell a Great Data Story in 4 Steps

Best Science PowerPoint Templates and Slide Design Examples

Free Research Poster Templates and Tutorials

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Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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How to Structure a Scientific Article, Conference Poster and Presentation

• First Online: 22 April 2022

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• Sarah Cuschieri 2  

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The main aim of any research endeavor is to disseminate research findings among the scientific community and enrich scientific knowledge. The two most common scientific dissemination modes are journal articles and conference proceedings. Whichever route you decide to follow, the scientific writing that needs to be partaken typically follows the IMRaD format, i.e., Introduction, Methods, Results, and Discussion-Conclusion. This format is applicable for abstracts (unless an unstructured abstract is specifically required), scientific research articles, and conference poster or oral presentations. Understanding the importance of each IMRaD section along with the role of a clear and informative title is your road to success. This also applies to proper citation of other people’s work, ideas, and thoughts. This chapter will guide you, through a stepwise approach, to understand the various elements that make up a successful scientific article, an abstract, a conference poster, and an oral presentation.

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• 01 December 2021

How to tell a compelling story in scientific presentations

• Bruce Kirchoff 0

Bruce Kirchoff is a botanist and storyteller at the University of North Carolina at Greensboro in North Carolina, USA. His new book is Presenting Science Concisely .

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Structuring your presentations with care can help you to clearly communicate to your audience. Credit: Getty

Scientific presentations are too often boring and ineffective. Their focus on techniques and data do not make it easy for the audience to understand the main point of the research.

If you want to reach beyond the narrow group of scientists who work in your specific area, you need to tell your audience members why they should be interested. Three things can help you to be engaging and convey the importance of your research to a wide audience. I had been teaching scientific communication for several years when I was approached to write a book about improving scientific presentations 1 . These are my three most important tips.

State your main finding in your title

The best titles get straight to the point. They tell the audience what you found, and they let them know what your talk will be about. Throughout this article, I will use titles from Nature papers published in the past two years as examples that will stand in for presentation titles. This is because Nature articles have a similar goal of attempting to make discipline-specific research available to a broader audience of scientists. Take, for example: ‘Supply chain diversity buffers cities against food shocks’ 2 .

A great title tells the reader exactly what’s new and precisely conveys the main result, as this one demonstrates. A more conventional title would have been ‘Effect of supply chain diversity on food shocks’, which omits the direction of the effect — so mainly scientists who are interested in your research area will be attracted to the talk. Others will wonder whether the talk will be a waste of time: maybe there was no effect at all.

Collection: Careers toolkit

Another example of a good title is: ‘Organic management promotes natural pest control through altered plant resistance to insects’ 3 .

This title ensures that the audience members know that the talk will be about the beneficial effects of organic crop management before they hear it. They also know that organic management increases plant resistance to insects. This title is much better than one such as: ‘Effects of organic pest management on plant insect resistance’. This title tells the audience the general area of the talk but does not give them the main result.

Finally, look at: ‘A highly magnetized and rapidly rotating white dwarf as small as the Moon’ 4 .

Good titles can just as easily be written for descriptive work as for experimental results. All you need to do is tell your audience what you found. Be as specific as possible. Compare this title with a more conventional one for the same work: ‘Use of the Zwicky Transient Facility to search for short period objects below the main white dwarf cooling sequence’. This title might be of interest to astronomers interested in using this facility, but is unlikely to attract anyone beyond them.

‘But’ is good — use it for dramatic effect

The contradiction implied by the word ‘but’ is one of the most powerful tools a scientist can use 5 . Contradictions introduce problems and provide dramatic effect, tension and a reason to keep listening.

Without such contradictions, the talk will consist of a bunch of results strung together in a seemingly endless and mind-numbing list. We can think of this list as a series of ‘and’ statements: “We did this and this and ran this experiment and found this result and . . . and . . . and.”

Contrast this with a structure that begins with a few important facts, tethered by ands, and then introduces the problem to be solved. Finally, ‘therefore’ can introduce results or subsequent actions. That structure would look like this: ‘X is the current state of knowledge, and we know Y. But Z problem remains. Therefore, we carried out ABC research.’ The introduction of even one contradiction wakes up people in the audience and helps them to focus on the results.

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A paper published earlier this year on SARS-CoV-2 and host protein synthesis provides an excellent example of the narrative form using ‘and’, ‘but’ and ‘therefore’ 6 . In the example below, I have shortened the abstract and simplified the transitions, but maintained the authors’ original structure 6 . Although they did not use ‘but’ or ‘therefore’ in their abstract, the existence of these terms is clearly implied. I have made them explicit in the following rendition.

“Coronaviruses have developed a variety of mechanisms to repress host messenger RNA translation and to allow the translation of viral mRNA and block the cellular immune response. But a comprehensive picture of the effects of SARS-CoV-2 infection on cellular gene expression is lacking. Therefore, we combine RNA sequencing, ribosome profiling and metabolic labelling of newly synthesized RNA to comprehensively define the mechanisms that are used by SARS-CoV-2 to shut off cellular protein synthesis.”

In this example, background information is given in the first sentence, linked by a series of conjunctions. Then the problem is introduced — this is the contradiction that comes with ‘but’. The solution to this problem is given in the next sentence (and introduced by using ‘therefore’). This structure makes the text interesting. It will do the same for your presentations.

Use repeated problems and solutions to create a story

Use the power of contradiction to maintain audience engagement throughout your talk. You can string together a series of problems and solutions (buts and therefores) to create a story that leads to your main result. The result highlighted in your title will help you to focus your talk so that the solutions you present lead to this overarching result.

Here is the general pattern:

1. Present the first part of your results.

2. Introduce a problem that remains.

3. Provide a solution to this problem by presenting more results.

4. Introduce the next problem.

5. Present the results that address this problem.

6. Continue this ‘problem and solution’ process through your presentation.

7. End by restating your main finding and summarize how it arises from your intermediate results.

The SARS-CoV-2 abstract 6 uses this pattern of repeated problems (buts) and solutions (therefores). I have modified the wording to clarify these sections.

1. Result 1: SARS-CoV-2 infection leads to a global reduction in translation, but we found that viral transcripts are not preferentially translated.

2. Problem 1: How then does viral mRNA comes to dominate the mRNA pool?

3. Solution 1: Accelerated degradation of cytosolic cellular mRNAs facilitates viral takeover of the mRNA pool in infected cells.

4. Problem 2: How is the translation of induced transcripts affected by SARS-CoV-2 infection?

5. Solution 2: The translation of induced transcripts (including innate immune genes) is impaired.

6. Problem 3: How is translation impaired? What is the mechanism?

7. Solution 3: Impairment is probably mediated by inhibiting the export of nuclear mRNA from the nucleus, which prevents newly transcribed cellular mRNA from accessing ribosomes.

8. Final summary: Our results demonstrate a multipronged strategy used by SARS-CoV-2 to take over the translation machinery and suppress host defences.

Using these three basic tips, you can create engaging presentations that will hold the attention of your audience and help them to remember you. For young scientists, especially, that is the most important thing the audience can take away from your talk.

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This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged .

Kirchoff, B. Presenting Science Concisely (CABI, 2021).

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Gomez, M., Mejia, A., Ruddell, B. L. & Rushforth, R. R. Nature 595 , 250–254 (2021).

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Tips for Scientific Writing & Presentation

The following resources will assist students with scientific writing and presentation skills including how to write an abstract and create a poster for a conference.

Abstract Guidelines

• Abstract Guidelines for the Medical Student Research Symposium

BU Authorship Guidelines

• What does it mean to be an author?

Manuscript Tips

• Me write pretty one day: how to write a scientific paper
• Improving your scientific writing: a short guide
• Online writing lab
• Bibliography software (Zotero has a lot of advantages (including that it is free).

Poster Tips

• Tips on Poster Design & Presentation
• Presentation Skills Toolkit for Medical Students (resources on developing and delivering formal lectures and presentations, poster and oral abstract presentations, patient presentations, and leading small group sessions)
• Conference presentations: Lead the poster parade
• Posters and Presentations (BU Alumni Medical Library—template here)
• Poster Template 2023 (template to create your poster; includes BMC and BU logos)

Printing Your Poster

• Fedex (700 Albany St., Boston) will print posters at a discount (tell them you are from BU). Most posters are printed on photo satin paper and can be emailed as a PDF to this Fedex location using the following email: [email protected] .
• PhD Posters
• Poster Presentations
• Posters should be created as a PowerPoint file at final size (check your meeting for dimensions; 4′ wide x 3′ tall is common).

Presentation Tips

• Three tips for giving a great research talk
• How to Speak (Patrick Winston’s famous MIT lecture)

If you have found other great resources please let us know .

Scientific Writing: Structuring a scientific article

• Resume/Cover letter
• Structuring a scientific article
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How to Structure a Scientific Article

Many scientific articles include the following elements:

I. Abstract: The abstract should briefly summarize the contents of your article. Be sure to include a quick overview of the focus, results and conclusion of your study.

II. Introduction:  The introduction should include any relevant background information and articulate the idea that is being investigated. Why is this study unique? If others have performed research on the topic, include a literature review. 

III. Methods and Materials:  The methods and materials section should provide information on how the study was conducted and what materials were included. Other researchers should be able to reproduce your study based on the information found in this section. 

IV. Results:  The results sections includes the data produced by your study. It should reflect an unbiased account of the study's findings. 

V.  Discussion and Conclusion:  The discussion section provides information on what researches felt was significant and analyzes the data. You may also want to provide final thoughts and ideas for further research in the conclusion section. 

For more information, see How to Read a Scientific Paper.  

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How to Compose, Write and Publish a Scientific or Professional Communication

Milivoj boranic.

Professor of Pediatrics and Oncology, retired from Institute “Rudjer Boskovic”, Zagreb, Croatia and Faculty of medicine, Osijek, Croatia (till 2004) and Faculty of medicine, Mostar, Bosnia and Herzegovina (till 2013). ORCID ID: http://www.orcid.org/0000-0003-2780-7165

There is an ample number of recommendations, guides and monographs addressing the art of composing and publishing written, oral or visual communications in science and various professions. In order to write an article, autors have to follow certain rules. Presentation of the article (oral or poster presentation) also requires skill, meaning that you have to fulfill certain guidelines and regulations.

1. INTRODUCTION

There is an ample number of recommendations, guides and monographs addressing the art of composing and publishing written, oral or visual communications in science and various professions (see e. g. references 1-8).

Useful guidelines in our language are available on the internet, in monographs and as the instructions to authors in medical journals (e. g., see references 9-18). On the basis those guidelines, as well as from personal experience that I have acquired as author, mentor, reviewer, and above all as a chairman of the committee evaluating doctor-of-science and master-of-science theses at a medical faculty, I am glad, for this occasion, to present a personal view of essential steps in producing useful communications.

2. STEPS IN ARTICLE WRITING

Before embarking on the composing of a scientific or professional communication, observe following considerations:

• What is the reason for your effort? Is there anything important or new that you would like to convey? Are you forced to write an article or deliver an oral presentation for the sake of personal promotion? Is the communication needed for your professional advancement? “Forced” communications are likely to miss the point.
• Read relevant literature covering the field of your interest. Access journal databases (eg. PubMed, Web of Science, or Scopus (authorization is needed for the latter two). Ask for help your library service, if needed and if available. Pay attention to relevant and up-to-date communications. Consider meta-analyses. Information about actual knowledge and/or problems provides your work with appropriate background. Keep record of relevant articles, save their abstracts and/or jot down their summaries emphasizing points of your particular interest. This material will be used later when writing the Introduction and discussion of your communication.
• Collect your data and summarize them in tables and/or graphs. Provide approipriate legends. Carry out statistical analysis. Try to make the tables and/or graphs (with their legends) self-explanatory. If needed, choose appropriate illustrations (scans, histology, etc) and provide explanatory legends. Keep eye on that material when writing the Results section.
• Now set out to compose your communication by starting with the Summary, not the Introduction! This will help you make a general idea of what you are going to communicate before embarking on having it on paper in extenso. The summary may be amended later, after completing the communication. This summary may also be used with the applications for scientific or professional meetings.
• Compose the Material and Methods section. This helps you get going. Be meticulous, pay attention to details. Avoid detailed description of well-known routine procedures. Provide name(s) and addresse(s) of the manufactures and vendors. Keep in mind that detailed description of materials and methods shows your scientific sincerity.
• Writing the Introduction is a demanding task. NEVER start by writing it the first. Otherwise you get entangled with (often) useles reiteration of known facts and are likely to lose momentum before embarking on the results. Write the Introduction after the Results section. Avoid redundant reiteration of common knowledge, give only a general outline of well-known facts; concentrate on the area of your work. Describe current state-of-the-art in your area of interest and emphasize problems, unsolved questions and controversies. Here you may resort to the material (abstracts, summaries) acquired by means of the literature search (see section 2).
• Describe the Results looking at your tables, graphs and/or pictures. The material should be self-explanatory so that the text itself may point out to the essential facts only. Emphasize major findings.
• Discussion should put your findings, observations or research into the perspective of the knowledge and facts outlined in the Introduction. Do not repeat results, rather explain and comment them. Concentrate on your contribution to the field. Discuss controversies.
• Literature should be organized according to the Vancouver system. Be meticulous with citing! Avoid unnecessary or redundant citations. Consult the official list of journal title abbreviations.
• Add Summary written before; if needed, improve it. Provide standard keywords (see Medical Standard Headings - MeSH). If needed, add summary in the national language (as required e.g. for dissertations) and provide translated MeSH titles.
• Have your text edited by a language professional.
• If you intend to publish a scientific article, choose appropriate journal taking into account its rank and scope. Consult the list of journals with their impact factors at. See useful advice at. Do not hesitate to proffer your manuscript to a good journal having a high impact factor; the editor may turn it down, but you are likely to receive useful review(s). Improved paper may then be profferred to a less prestigious journal. Do not proffer your paper simultaneously to two journals! It is allowed, however, to publish in domestic journal a translation of an article already published in an international journal, with proper reference to the original.

3. ORAL PRESENTATION

• Organize your speech so as to observe the allotted time limit. Exercise aloud in advance!
• If you use the PowerPoint, make the written text on slides as succint as possible and legible from distance. Do not clog the slides with redundant text in small letters! Simplify tables and graphs, make them easily legible and understandable. Complicated graphs and tables with too many items cannot be understood by the audience. Avoid too many fonts, colours and other embellishments.
• When speaking, address the audience and not the screen!
• Summarize your presentation with a succint repetition of your message

See useful advices in references 19 and 20.

4. POSTER PRESENTATIONS - GUIDELINES

• Make the posters self-explanatory and legible from reasonable distance.
• Emphasize the Aim and Conclusions of your work.
• Do not clog the poster with differing fonts, too many colors and other embellishments, since that distracts from the message.
• A short and easily legible abstract may be added.

See numerous advices, e.g., in references 21 - 25.

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New database features 250 AI tools that can enhance social science research

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AI – or artificial intelligence – is often used as a way to summarize data and improve writing. But AI tools also represent a powerful and efficient way to analyze large amounts of text to search for patterns. In addition, AI tools can assist with developing research products that can be shared widely.

It’s with that in mind that we , as researchers in social science , developed a new database of AI tools for the field . In the database, we compiled information about each tool and documented whether it was useful for literature reviews, data collection and analyses, or research dissemination. We also provided information on the costs, logins and plug-in extensions available for each tool.

When asked about their perceptions of AI, many social scientists express caution or apprehension. In a sample of faculty and students from over 600 institutions, only 22% of university faculty reported that they regularly used AI tools .

From combing through lengthy transcripts or text-based data to writing literature reviews and sharing results, we believe AI can help social science researchers – such as those in psychology, sociology and communication – as well as others get the most out of their data and present it to a wider audience.

Analyze text using AI

Qualitative research often involves poring over transcripts or written language to identify themes and patterns. While this kind of research is powerful, it is also labor-intensive. The power of AI platforms to sift through large datasets not only saves researchers time, but it can also help them analyze data that couldn’t have been analyzed previously because of the size of the dataset.

Specifically, AI can assist social scientists by identifying potential themes or common topics in large, text-based data that scientists can interrogate using qualitative research methods. For example, AI can analyze 15 million social media posts to identify themes in how people coped with COVID-19. These themes can then give researchers insight into larger trends in the data, allowing us to refine criteria for a more in-depth, qualitative analysis.

AI tools can also be used to adapt language and scientists’ word choice in research designs. In particular, AI can reduce bias by improving the wording of questions in surveys or refining keywords used in social media data collection.

Identify gaps in knowledge

Another key task in research is to scan the field for previous work to identify gaps in knowledge. AI applications are built on systems that can synthesize text . This makes literature reviews – the section of a research paper that summarizes other research on the same topic – and writing processes more efficient.

Research shows that human feedback to AI, such as providing examples of simple logic, can significantly improve the tools’ ability to perform complex reasoning . With this in mind, we can continually revise our instructions to AI and refine its ability to pull relevant literature.

However, social scientists must be wary of fake sources – a big concern with generative AI . It is essential to verify any sources AI tools provide to ensure they come from peer-reviewed journals.

Share research findings

AI tools can quickly summarize research findings in a reader-friendly way by assisting with writing blogs, creating infographics and producing presentation slides and even images.

Our database contains AI tools that can also help scientists present their findings on social media. One tool worth highlighting is BlogTweet . This free AI tool allows users to copy and paste text from an article like this one to generate tweet threads and start conversations.

Be aware of the cost of AI tools

Two-thirds of the tools in the database cost money. While our primary objective was to identify the most useful tools for social scientists, we also sought to identify open-source tools and curated a list of 85 free tools that can support literature reviews, writing, data collection, analysis and visualization efforts.

In our analysis of the cost of AI tools, we also found that many offer “freemium” access to tools. This means you can explore a free version of the product. More advanced versions of the tool are available through the purchase of tokens or subscription plans.

For some tools, costs can be somewhat hidden or unexpected. For instance, a tool that seems open source on the surface may actually have rate limits, and users may find that they’ve run out of free questions to ask the AI.

The future of the database

Since the release of the Artificial Intelligence Applications for Social Science Research Database on Oct. 5, 2023, it has been downloaded over 400 times across 49 countries. In the database, we found 131 AI tools useful for literature reviews, summaries or writing. As many as 146 AI tools are useful for data collection or analysis, and 108 are useful for research dissemination.

We continue to update the database and hope that it can aid academic communities in their exploration of AI and generate new conversations. The more that social scientists use the database, the more they can work toward consensus of adopting ethical approaches to using AI in research and analysis.

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With AI writing so much code, should you still study computer science? This new data point provides an answer.

• UC Berkeley sees a 48% jump in first-year applications to study computer science.
• Despite generative AI advances, students are eager to pursue computer science careers.
• Human developers remain essential for creating something new.

One of the most persistent concerns around generative AI is whether the technology will put workers out of a job. This idea has particularly caught on in the context of software coding .

GitHub Copilot can write a lot of code these days, so is it even worth studying computer science now? That's been a question on the minds of math-minded high schoolers since ChatGPT burst on the scene in 2022.

There's a new data point that helps answer at least part of this question: Students are still lining up in droves to take computer science in college.

An eye-popping data point

Let's take The University of California Berkeley as an example, as this college at or near the top for computer science.

First-year applications to UC Berkeley's College of Computing, Data Science, and Society CDSS increased 48% this year. There were 14,302 (non-transfer) applications for these CDSS majors in the Fall 2024 incoming class, versus 9,649 the previous year.

For context, the number of first-year applications to UC Berkeley as a whole didn't change much from a year earlier.

Related stories

This was announced last week by Professor Jennifer Chayes, the dean of Berkeley's College of CDSS. She popped these eye-popping stats during a fireside chat with Governor Gavin Newsom and Stanford Professor Fei-Fei Li at the at the Joint California Summit on Generative AI in San Francisco.

There's a role for human software developers

Afterwards, I got in touch with John DeNero, Computer Science Teaching Professor at UC Berkeley, to talk about this some more.

He's also chief scientist at Lilt , a generative AI startup, and he was previously a researcher at Google working on Google Translate , one of the first successful AI-powered consumer apps.

"Students express some concern that generative AI will affect the software engineering job market, especially for entry-level positions, but they are still excited about careers in computing," he wrote in an email to Business Insider. "I tell them that I think many of the challenging aspects of software development can't be performed reliably by generative AI at this point, and that I expect there will still be a central role for human software developers long into the future."

AI can't do new things very well

Generative AI is currently very good at replicating parts of software programs that have been written many times before, DeNero explained.

That includes computer science homework assignments! See BI's coverage on how much ChatGPT is used to cheat on homework .

What if you want to create something new? This is where smart human coders will still be needed. (This makes logical sense as AI models are trained on data. If that information doesn't exist yet or it's not part of the training dataset, the models often get in trouble).

Generative AI "requires a lot of thoughtful human intervention to produce something new, and all consequential software development projects involve quite a bit of novelty," DeNero said. "That's the hard and interesting part of computing that currently requires clever and well-trained people."

"Generative AI can speed up the more mundane parts of software development, and software developers tend to adopt efficiency tools quickly," he added.

What happens at Lilt?

This applies to what's happening at Lilt, which is building an AI platform for translators.

Google Translate first came out 18 years ago. And still, human linguists have jobs and are relied upon when translations are really important. For instance, you can use Google Translate to read a Japanese train timetable maybe, but would you use the app to translate your business's most important contract without having a human expert check it? Probably not.

"To reliably produce publication-quality translations, human expert linguists are still at the center of the process, but by using Lilt's task-specific generative AI models, those experts are much faster, more accurate, and more consistent," DeNero said. "As a result, more text gets translated at higher quality into more languages."

He expects this same pattern to play out in software development: A small team of highly trained human developers will have an even greater capacity to build useful high-quality software.

"And so, future Berkeley graduates will have plenty of opportunities to use their computing skills to improve the world," DeNero said. "Hopefully some more of them will come work for Lilt."

Watch: AI expert discusses generative AI: What it means and how it will impact our future

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April 5, 2024

AI Chatbots Will Never Stop Hallucinating

Some amount of chatbot hallucination is inevitable. But there are ways to minimize it

By Lauren Leffer

Moor Studio/Getty Images

Last summer a federal judge fined a New York City law firm $5,000 after a lawyer used the artificial intelligence tool ChatGPT to draft a brief for a personal injury case. The text was full of falsehoods —including more than six entirely fabricated past cases meant to establish precedent for the personal injury suit. Similar errors are rampant across AI-generated legal outputs, researchers at Stanford University and Yale University found in a recent preprint study of three popular large language models (LLMs). There’s a term for when generative AI models produce responses that don’t match reality: “hallucination.”

Hallucination is usually framed as a technical problem with AI—one that hardworking developers will eventually solve. But many machine-learning experts don’t view hallucination as fixable because it stems from LLMs doing exactly what they were developed and trained to do: respond, however they can, to user prompts. The real problem, according to some AI researchers, lies in our collective ideas about what these models are and how we’ve decided to use them. To mitigate hallucinations, the researchers say, generative AI tools must be paired with fact-checking systems that leave no chatbot unsupervised.

Many conflicts related to AI hallucinations have roots in marketing and hype. Tech companies have portrayed their LLMs as digital Swiss Army knives, capable of solving myriad problems or replacing human work. But applied in the wrong setting, these tools simply fail. Chatbots have offered users incorrect and potentially harmful medical advice, media outlets have published AI-generated articles that included inaccurate financial guidance , and search engines with AI interfaces have invented fake citations . As more people and businesses rely on chatbots for factual information, their tendency to make things up becomes even more apparent and disruptive.

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But today’s LLMs were never designed to be purely accurate. They were created to create—to generate —says Subbarao Kambhampati , a computer science professor who researches artificial intelligence at Arizona State University. “The reality is: there’s no way to guarantee the factuality of what is generated,” he explains, adding that all computer-generated “creativity is hallucination, to some extent.”

In a preprint study released in January, three machine-learning researchers at the National University of Singapore presented a proof that hallucination is inevitable in large language models. The proof applies some classic results in learning theory, such as Cantor’s diagonalization argument , to demonstrate that that LLMs simply cannot learn all computable functions. In other words, it shows that there will always be solvable problems beyond a model’s abilities. “For any LLM, there is a part of the real world that it cannot learn, where it will inevitably hallucinate,” wrote study co-authors Ziwei Xu, Sanjay Jain and Mohan Kankanhalli in a joint e-mail to Scientific American .

Although the proof appears to be accurate, Kambhampati says, the argument it makes—that certain difficult problems will always stump computers—is too broad to provide much insight into why specific confabulations happen. And, he continues, the issue is more widespread than the proof shows because LLMs hallucinate even when faced with simple requests.

One main reason AI chatbots routinely hallucinate stems from their fundamental construction, says Dilek Hakkani-Tür , a computer science professor who studies natural language and speech processing at the University of Illinois at Urbana-Champaign. LLMs are basically hyperadvanced autocomplete tools; they are trained to predict what should come next in a sequence such as a string of text. If a model’s training data include lots of information on a certain subject, it might produce accurate outputs. But LLMs are built to always produce an answer, even on topics that don’t appear in their training data. Hakkani-Tür says this increases the chance errors will emerge.

Adding more factually grounded training data might seem like an obvious solution. But there are practical and physical limits to how much information an LLM can hold, says computer scientist Amr Awadallah , co-founder and CEO of the AI platform Vectara, which tracks hallucination rates among LLMs on a leaderboard. (The lowest hallucination rates among tracked AI models are around 3 to 5 percent .) To achieve their language fluency, these massive models are trained on orders of magnitude more data than they can store—and data compression is the inevitable result. When LLMs cannot “recall everything exactly like it was in their training, they make up stuff and fill in the blanks,” Awadallah says. And, he adds, these models already operate at the edge of our computing capacity; trying to avoid hallucinations by making LLMs larger would produce slower models that are more expensive and more environmentally harmful to operate.

Another cause of hallucination is calibration, says Santosh Vempala , a computer science professor at the Georgia Institute of Technology. Calibration is the process by which LLMs are adjusted to favor certain outputs over others (to match the statistics of training data or to generate more realistically human-sounding phrases).* In a preprint paper first released last November, Vempala and a coauthor suggest that any calibrated language model will hallucinate —because accuracy itself is sometimes at odds with text that flows naturally and seems original. Reducing calibration can boost factuality while simultaneously introducing other flaws in LLM-generated text. Uncalibrated models might write formulaically, repeating words and phrases more often than a person would, Vempala says. The problem is that users expect AI chatbots to be both factual and fluid.

Accepting that LLMs may never be able to produce completely accurate outputs means reconsidering when, where and how we deploy these generative tools, Kambhampati says. They are wonderful idea generators, he adds, but they are not independent problem solvers. “You can leverage them by putting them into an architecture with verifiers,” he explains—whether that means putting more humans in the loop or using other automated programs.

At Vectara, Awadallah is working on exactly that. His team’s leaderboard project is an early proof of concept for a hallucination detector—and detecting hallucinations is the first step to being able to fix them, he says. A future detector might be paired with an automated AI editor that corrects errors before they reach an end user. His company is also working on a hybrid chatbot and news database called AskNews, which combines an LLM with a retrieval engine that picks the most relevant facts from recently published articles to answer a user’s question. Awadallah says AskNews provides descriptions of current events that are significantly more accurate than what an LLM alone could produce because the chatbot bases its responses only on the sources dredged up by the database search tool.

Hakkani-Tür, too, is researching factually grounded systems that pair specialized language models with relatively reliable information sources such as corporate documents, verified product reviews, medical literature or Wikipedia posts to boost accuracy. She hopes that—once all the kinks are ironed out—these grounded networks could one day be useful tools for things like health access and educational equity. “I do see the strength of language models as tools for making our lives better, more productive and more fair,” she says.

In a future where specialized systems verify LLM outputs, AI tools designed for specific contexts would partially replace today’s all-purpose models. Each application of an AI text generator (be it a customer service chatbot, a news summary service or even a legal adviser) would be part of a custom-built architecture that would enable its utility. Meanwhile less- grounded generalist chatbots would be able to respond to anything you ask but with no guarantee of truth. They would continue to be powerful creative partners or sources of inspiration and entertainment—yet not oracles or encyclopedias—exactly as designed.

* Editor’s Note (4/5/24): This sentence was edited after posting. It previously stated that mitigating bias in a large language model’s output is an example of calibration. That is instead a separate process known as alignment.