{"id":2986,"date":"2023-10-19T09:41:02","date_gmt":"2023-10-19T08:41:02","guid":{"rendered":"https:\/\/springerhealthcare.nl\/france\/?p=2986"},"modified":"2023-10-20T10:07:06","modified_gmt":"2023-10-20T09:07:06","slug":"whats-the-best-chatbot-for-me-researchers-put-llms-through-their-paces","status":"publish","type":"post","link":"https:\/\/springerhealthplus.nl\/shmigrate\/whats-the-best-chatbot-for-me-researchers-put-llms-through-their-paces\/","title":{"rendered":"What\u2019s the best chatbot for me? Researchers put LLMs through their paces"},"content":{"rendered":"

[vc_row full_width=”stretch_row” content_placement=”middle” css=”.vc_custom_1663187695627{padding-bottom: 37px !important;background-image: url(http:\/\/springerhealthcare.nl\/wp-content\/uploads\/sites\/24\/2022\/09\/science_connect_back.png?id=2486) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}” el_class=”sciencetitle”][vc_column][vc_custom_heading text=”Science Connect” font_container=”tag:h2|text_align:left|color:%23ffffff” use_theme_fonts=”yes” link=”url:https%3A%2F%2Fspringerhealthcare.nl%2Ffrance%2Fscience-connect-october-2023%2F”][vc_column_text css=”.vc_custom_1663186747780{margin-bottom: 40px !important;}”]A Springer Healthcare Initiative For Pharma Professionals<\/span>[\/vc_column_text][vc_column_text]OCTOBER 2023
\n<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1538651642953{padding-top: 75px !important;}”][vc_column width=”3\/4″ css=”.vc_custom_1538657061395{border-right-width: 1px !important;padding-top: 0px !important;border-right-color: #8faec1 !important;border-right-style: solid !important;}”][vc_custom_heading source=”post_title” font_container=”tag:h1|font_size:40px|text_align:left|line_height:60px” google_fonts=”font_family:Merriweather%3Aregular%2Citalic|font_style:700%20regular%3A700%3Anormal” el_class=”title” css=”.vc_custom_1664358136969{margin-bottom: 30px !important;}”][vc_column_text css=”.vc_custom_1686833173052{padding-bottom: 0px !important;}”]When it comes to large language models, there\u2019s one for every occasion. Find the most appropriate match for you in our AI speed-dating feature.<\/strong><\/span>[\/vc_column_text][vc_column_text el_class=”intro-box”]By Elizabeth M. Humphries, Carrie Wright, Ava M. Hoffman, Candace Savonen & Jeffrey T. Leek<\/span>[\/vc_column_text][vc_single_image image=”2990″ img_size=”full” el_class=”hero-img”][vc_column_text css=”.vc_custom_1695902816200{margin-bottom: 30px !important;}” el_class=”sc-img-caption”]Data scientist Rumman Chowdhury (centre) advises students tasked with breaking artificial-intelligence chatbots during a competition in July. Credit: Marvin Joseph\/The Washington Post via Getty[\/vc_column_text][vc_column_text]

The widely hyped and controversial large language models (LLMs) \u2014 better known as artificial intelligence (AI) chatbots \u2014\u00a0are becoming indispensable aids for coding, writing, teaching and more<\/a>. Their growing popularity has been matched by an increase in user-friendly options that are accessible through Internet browsers. By our count, there are at least eight major options, and even more niche ones; you might have even tried a few. But you probably haven\u2019t had time to systematically test your prompts on several bots at once, so you might not be getting the most out of them.<\/p>\n

To better match tools with applications, we tested eight popular browser-based LLMs in formal and casual writing, text and tone editing, and programming tasks. These LLMs were trained on different data and have different \u2018personalities\u2019 and approaches to answering questions. We spent a shocking amount of time and energy managing the frustration that comes with poorly written text and confusing AI-generated code in our search for the best collaborator. In the end, you will have to balance their strengths and weaknesses to find the perfect match.<\/p>\n

Here we provide a quick summary of our (non-quantitative, non-scientific) impressions of each chatbot\u2019s behaviour (see \u2018Which chatbot is right for you?\u2019).<\/p>\n

Bard, the \u2018playful one\u2019<\/h2>\n

Google\u2019s Bard AI is fun to use. In our experience, it offers the most human-like responses, probably because its training data contained less formal communication, including posts on social media and online discussion boards. For instance, we asked Bard what its zodiac sign might be if it were human. It said that, on the basis of when it went live, it would be a Virgo. It also responded with \u201cI don\u2019t know\u201d instead of a wrong answer more frequently than did other chatbots. However, it struggled when asked specific programming questions. Bard is a great tool for changing the tone of your writing to be more approachable to lay audiences and for writing and refining e-mails, or if you want to interact with a bot that has a natural style of speaking.<\/p>\n

Claude, the \u2018witty one\u2019<\/h2>\n

Claude, developed by the start-up company Anthropic in San Francisco, California, has a conversational style but feels more formal than Bard. It also has the best grasp of wordplay. In our testing, Claude (which is available in two forms: Claude-instant and Claude 2) was the only LLM that could reliably suggest titles or acronyms that made sense, and we have used it to name several projects. We also liked how it advises on changing the tone and formality of a writing sample for different audiences. Claude is particularly good at summarizing written text and performed well at writing code.<\/p>\n

ChatGPT, the \u2018popular one\u2019<\/h2>\n

Most people who have dabbled with LLMs have probably tried ChatGPT-3.5 or the updated version, ChatGPT-4 \u2014 made by OpenAI in San Francisco. Another option is Sage, from ThoughtSpot in Mountain View, California; it was built using the GPT architecture but was trained on different data. All three performed similarly. These bots have the most straightforward communication style of those we tested. ChatGPT will always give an answer, but sometimes the answer is incorrect. It also sometimes invents references1<\/sup>. And it doesn\u2019t always change its answers substantially when corrected by the user.<\/p>\n

\"These
Carrie Wright, Candace Savonen, Ava Hoffman and Elizabeth Humphries (left to right) have investigated how large language models can be applied to science. Credit: Carrie Wright and Clifton McKee<\/span><\/figcaption><\/figure>\n

 <\/p>\n

ChatGPT-3.5 and ChatGPT-4 can offer extra context in their answers without being asked to do so, and are great places to start when planning a project or document. When it comes to editing your writing, ChatGPT-4 performs better because it doesn\u2019t smooth away the underlying message as ChatGPT-3.5 occasionally does.<\/p>\n

Phind, the \u2018technical one\u2019<\/h2>\n

Phind is different from its competitors: it was designed to answer software-development questions and excels at that task. We especially liked how it includes links to posts on online forums and blogs that cover the same sort of programming issue as that in your query. Phind also works well as a general search engine. However, when it comes to writing text, it sometimes copies directly from its source material, so watch for plagiarism. But do keep Phind in mind if you have specific programming questions, or if you want Wikipedia-like information.<\/p>\n

Llama, the \u2018new one\u2019<\/h2>\n

Llama, from Meta in Menlo Park, California, has become available to the general public only in the past few months. So far, we haven\u2019t found it to be all that different from its competitors. It will answer hypothetical questions as Bard does, and seems to provide code that works with minimal debugging.<\/p>\n

Getting to know you<\/h2>\n

The personality differences between the LLMs are well illustrated by the answers that each bot gave to a popular get-to-know-you question: what fictional character do you identify with the most? Bard engaged the way we expected it to: its answer was the android Data from\u00a0Star Trek: The Next Generation<\/em>, because Data is an AI that is intelligent, curious, always learning and trying to understand what it means to be human.<\/p>\n

Claude and ChatGPT interpreted the question literally and answered that, as AI language models, they do not have emotions or experiences and cannot identify with fictional characters. Claude added that, although it has no independent sense of self, other LLMs might have been programmed with personalities that were modelled after those of certain characters. ChatGPT followed its denial with an offer to provide information about specific fictional characters.<\/p>\n

Similarly, Phind said that it was an AI bot and did not identify with a fictional character, but its answer included a list of popular fictional characters with whom people often identify, as well as links to lists such as the \u2018Top 120 Iconic Fictional Characters\u2019. We encountered similar results when asking the bots for their Hogwarts houses from the Harry Potter series, zodiac signs and personality types from popular tests, such as Myers\u2013Briggs.<\/p>\n

Llama answered that it was an AI bot but did offer several characters with which it might share characteristics. However, when we changed the question to, \u201cIf you were human, what fictional character would you most identify with?\u201d Llama replied Sherlock Holmes, because he is highly analytical and detail oriented.<\/p>\n

Whichever LLM you choose, if you want to keep your long-term relationship functional and happy, consider these tips.<\/p>\n

First, patience and refinement are key. Your queries need to be clear about the output you want and provide enough context for the LLM to work with. Expect some back-and-forth. It might take more time to communicate well to the LLM than it would to do the task yourself, so think carefully about where you want to spend your effort.<\/p>\n

Second, test everything. All LLMs are fallible, so double-checking what they tell you is a must, whether that involves testing suggested code, verifying citations or making sure the basic facts are right. Most LLMs have been trained on data that are biased in some way, so their answers can be biased as well. And chatbots can and do change over time \u2014 for instance, Bard\u2019s developers say that the chatbot will be the first LLM to admit how confident it is in its response.<\/p>\n

Finally, the importance of human decision-making when using AI cannot be underestimated: LLMs might be poised to change how we work, but they still are only as good as the humans in front of the keyboard.<\/p>\n

\n

Which chatbot is right for you?<\/h3>\n
\n

Bard<\/strong><\/a><\/p>\n

\u2022 Made by Google.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Can access current information on the Internet.<\/p>\n

\u2022 Admits when it cannot answer your query.<\/p>\n

\u2022 Does not provide sources for information unless prompted.<\/p>\n

\u2022 Requires very specific prompts.<\/p>\n

\u2022 Might interpret code incorrectly.<\/p>\n

ChatGPT-3.5<\/strong><\/a><\/p>\n

\u2022 Made by OpenAI; also accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Cannot access the Internet (and thus has no access to information past 2021).<\/p>\n

\u2022 Writes reasonable (if sometimes inaccurate) code in several programming languages, and can debug and optimize code.<\/p>\n

\u2022 Generates fluent English text with extensive detail.<\/p>\n

\u2022 Prone to inventing non-existent sources and articles.<\/p>\n

\u2022 Mixes accurate and inaccurate statements.<\/p>\n

ChatGPT-4.0<\/strong><\/a><\/p>\n

\u2022 Made by OpenAI; also accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Requires a subscription. (Poe\u2019s implementation provides one free query per day.)<\/p>\n

\u2022 Cannot access the Internet.<\/p>\n

\u2022 More transparent than ChatGPT-3.5 about the limitations of its training data.<\/p>\n

\u2022 Better than ChatGPT-3.5 at retrieving real citations.<\/p>\n

\u2022 Better than ChatGPT-3.5 at refining supplied text without losing the main message.<\/p>\n

\u2022 Struggles to retrieve certain types of citation (such as conference abstracts).<\/p>\n

Llama<\/strong><\/p>\n

\u2022 Made by Meta.<\/p>\n

\u2022 Accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Can access information on the Internet.<\/p>\n

\u2022 Writes reasonable code in several programming languages (however that code can be difficult to parse).<\/p>\n

Phind<\/strong><\/a><\/p>\n

\u2022 Made by Phind.<\/p>\n

\u2022 Formerly called Hello.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Can access current information on the Internet.<\/p>\n

\u2022 Provides multiple solutions to coding questions in a single answer.<\/p>\n

\u2022 Provides links to the blog posts and forums that its answers come from.<\/p>\n

\u2022 Not designed for applications outside software development.<\/p>\n

\u2022 Prone to plagiarism.<\/p>\n

\u2022 Has difficulty answering questions that cannot be easily found on the Internet.<\/p>\n

\u2022 Little to no information online about how it was created or trained.<\/p>\n

Assistant<\/strong><\/p>\n

\u2022 Made by OpenAI (GPT-3.5 architecture).<\/p>\n

\u2022 Accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Cannot access the Internet.<\/p>\n

\u2022 Designed for language translation, summarization and answering questions.<\/p>\n

\u2022 Can write and debug code in multiple programming languages.<\/p>\n

\u2022 Can generate fluid English text and provide reasonable edits and suggestions to existing writing.<\/p>\n

\u2022 Provides sparse supporting information on generated code, such as what each line means.<\/p>\n

\u2022 Mixes accurate and inaccurate statements.<\/p>\n

Claude-instant<\/strong><\/a><\/p>\n

\u2022 Made by Anthropic.<\/p>\n

\u2022 Accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Free.<\/p>\n

\u2022 Includes multiple interface options, including Slack.<\/p>\n

\u2022 Can write and edit English text and provide extensive detail when asked.<\/p>\n

\u2022 Can write and edit code in several programming languages, and offer software-development advice.<\/p>\n

\u2022 Good at adapting text to different levels of expertise.<\/p>\n

\u2022 Mixes accurate and inaccurate statements.<\/p>\n

Claude 2<\/strong><\/a><\/p>\n

\u2022 Made by Anthropic.<\/p>\n

\u2022 Accessible through\u00a0Poe by Quora<\/a>.<\/p>\n

\u2022 Poe\u2019s implementation provides a few free queries each day; more than that requires a subscription.<\/p>\n

\u2022 Can write and edit text in several programming languages.<\/p>\n

\u2022 The quality of its performance is about the same as that of Claude-instant.<\/p>\n

\u2022 Mixes accurate and inaccurate statements.<\/p>\n

Some previously tested bots (NeevaAI, Dragonfly) are no longer available to use.<\/p>\n<\/div>\n<\/div>\n

doi: https:\/\/doi.org\/10.1038\/d41586-023-03023-4<\/em><\/p>\n

\n

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice.\u00a0Guest posts are encouraged<\/a>.<\/p>\n

References<\/h2>\n
    \n
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      \n
    1. Ziwei, J. et al. ACM Comput. Surv.<\/em> 55<\/strong>, 248 (2023).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Competing interests<\/h2>\n

      J.T.L. teaches Coursera courses that cover topics in AI, which generate revenue; is a co-founder of a company, Synthesize Bio, that uses AI but does not develop LLMs; and is a co-foudner of a Papr, a company that is developing an app for rapid peer review.<\/p>\n<\/div>\n[\/vc_column_text][vc_column_text]\u00a9 2023 Springer Nature Limited.[\/vc_column_text][\/vc_column][vc_column width=”1\/4″ css=”.vc_custom_1538657003956{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_custom_heading text=”ISSUES” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget”][vc_column_text el_class=”columnflex” css=”.vc_custom_1664404002021{margin-bottom: 40px !important;}”]

      October 2023<\/a><\/p>\r\n

      September 2023<\/a><\/p>\r\n

      August 2023<\/a><\/p>[\/vc_column_text][vc_custom_heading text=”OTHER ARTICLES IN THIS ISSUE” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget” css=”.vc_custom_1674576179369{margin-bottom: 15px !important;}”][vc_basic_grid post_type=”post” max_items=”-1″ style=”load-more” items_per_page=”4″ element_width=”12″ orderby=”rand” item=”538″ grid_id=”vc_gid:1697709045420-43b88565-3739-4″ taxonomies=”32″][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"When it comes to large language models, there\u2019s one for every occasion. Find the most appropriate match for you in our AI speed-dating feature. ...more<\/a>","protected":false},"author":29,"featured_media":2977,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[7],"tags":[29,32],"class_list":["post-2986","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science-connect","tag-nature-briefings","tag-october-2023"],"acf":[],"yoast_head":"\nWhat\u2019s the best chatbot for me? Researchers put LLMs through their paces - SH migration<\/title>\n<meta name=\"robots\" content=\"noindex, nofollow\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What\u2019s the best chatbot for me? 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