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card"><div class="d-block position-relative mb-3 rounded-2 tease-thumbnail overflow-hidden"><img width="800" height="425" src="https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?resize=800%2C425" class="tease-thumbnail__img d-block width-full height-auto aspect-ratio-1032-548 object-fit-cover" alt="" decoding="async" srcset="https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?w=300 300w, https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?w=800 800w, https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?w=400 400w, https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?w=1032 1032w, https://github.blog/wp-content/uploads/2024/05/Enterprise-DarkMode-3.png?w=516 516w" sizes="(max-width: 800px) 100vw, 800px" /></div><a href="https://docs.github.com/en/get-started" class="d-block mb-2 f3 lh-condensed color-fg-default text-bold card__link">Get started with GitHub documentation</a><p 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</li> <li id="menu-item-78868" class="p-5 py-xl-7 px-xl-8 col-4 color-bg-subtle card"><div class="d-block position-relative mb-3 rounded-2 tease-thumbnail overflow-hidden"><img width="800" height="425" src="https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.42.45 AM.png?resize=800%2C425" class="tease-thumbnail__img d-block width-full height-auto aspect-ratio-1032-548 object-fit-cover" alt="" decoding="async" srcset="https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.42.45 AM.png?w=800 800w, https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.42.45 AM.png?w=400 400w, https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.42.45 AM.png?w=1032 1032w, https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.42.45 AM.png?w=516 516w" sizes="(max-width: 800px) 100vw, 800px" /></div><a href="https://github.blog/engineering/how-we-use-github-to-be-more-productive-collaborative-and-secure/" class="d-block mb-2 f3 lh-condensed color-fg-default text-bold card__link">How we use GitHub to be more productive, collaborative, and secure</a><p class="mb-3 f5 color-fg-muted">Our engineering and security teams do some incredible work. Let’s take a look at how we use GitHub to be more productive, build collaboratively, and shift security left.</p><span class="Link d-inline-flex flex-items-center" role="presentation">Learn more<svg xmlns="http://www.w3.org/2000/svg" class="octicon d-block ml-1 mt-1" viewBox="0 0 12 12" width="12" height="12"><path d="M4.7 10c-.2 0-.4-.1-.5-.2-.3-.3-.3-.8 0-1.1L6.9 6 4.2 3.3c-.3-.3-.3-.8 0-1.1.3-.3.8-.3 1.1 0l3.3 3.2c.3.3.3.8 0 1.1L5.3 9.7c-.2.2-.4.3-.6.3Z"></path></svg></span></li> </ul> </li> <li id="menu-item-78832"><a href="https://github.blog/enterprise-software/" class="position-relative d-flex flex-items-center flex-start no-wrap py-3 f4-mktg text-bold js-toggle" aria-controls="primary-78832-dropdown" aria-expanded="false" aria-haspopup="true" role="button">Enterprise software<svg viewBox="0 0 16 16" width="16" height="16" class="octicon d-block ml-1 mt-1 color-fg-muted" role="presentation"><path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 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color-fg-muted">Explore how to write, build, and deploy enterprise software at scale.</p></div> <ul class="d-flex flex-wrap list-style-none" aria-label="Enterprise software sub-menu"> <li id="menu-item-78833" class="col-6 d-block f4 color-fg-default color-border-subtle pr-4 pr-lg-8 border-right"><a href="https://github.blog/enterprise-software/automation/" class="d-block f4 color-fg-default text-bold">Automation</a><p class="mb-0 f5 color-fg-muted">Automating your way to faster and more secure ships.</p></li> <li id="menu-item-78834" class="col-6 d-block f4 color-fg-default color-border-subtle pl-4 pl-lg-8"><a href="https://github.blog/enterprise-software/ci-cd/" class="d-block f4 color-fg-default text-bold">CI/CD</a><p class="mb-0 f5 color-fg-muted">Guides on continuous integration and delivery.</p></li> <li id="menu-item-78835" class="col-6 d-block f4 color-fg-default color-border-subtle pr-4 pr-lg-8 border-right pt-2 pt-lg-4"><a 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https://github.blog/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-8.43.47 AM.png?w=516 516w" sizes="(max-width: 800px) 100vw, 800px" /></div><a href="https://resources.github.com/artificial-intelligence/how-enterprise-engineering-teams-can-successfully-adopt-ai/" class="d-block mb-2 f3 lh-condensed color-fg-default text-bold card__link">How enterprise engineering teams can successfully adopt AI</a><p class="mb-3 f5 color-fg-muted">Learn how to bring AI to your engineering teams and maximize the value that you get from it.</p><span class="Link d-inline-flex flex-items-center" role="presentation">Learn more<svg xmlns="http://www.w3.org/2000/svg" class="octicon d-block ml-1 mt-1" viewBox="0 0 12 12" width="12" height="12"><path d="M4.7 10c-.2 0-.4-.1-.5-.2-.3-.3-.3-.8 0-1.1L6.9 6 4.2 3.3c-.3-.3-.3-.8 0-1.1.3-.3.8-.3 1.1 0l3.3 3.2c.3.3.3.8 0 1.1L5.3 9.7c-.2.2-.4.3-.6.3Z"></path></svg></span></li> </ul> </li> <li id="menu-item-78840"><a href="https://github.blog/news-insights/" 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href="https://github.blog/ai-and-ml/llms/">LLMs</a></span></span></div> <h1 class="h3-mktg lh-condensed mb-3 color-fg-default">Demystifying LLMs: How they can do things they weren’t trained to do</h1> <div class="f4-mktg"> <p>Explore how LLMs generate text, why they sometimes hallucinate information, and the ethical implications surrounding their incredible capabilities.</p> </div> </div> <div class="offset-lg-1 col-lg-10"> <div class="position-relative overflow-hidden rounded-2 z-1"> <svg aria-hidden="true" width="1032" height="548" class=" d-block width-full height-auto" role="presentation"></svg> <img width="1200" height="630" src="https://github.blog/wp-content/uploads/2023/10/AI-DarkMode-3.png?resize=1200%2C630" class="d-block cover-image wp-post-image" alt="" decoding="async" loading="lazy" srcset="https://github.blog/wp-content/uploads/2023/10/AI-DarkMode-3.png?w=1200 1200w, https://github.blog/wp-content/uploads/2023/10/AI-DarkMode-3.png?w=300 300w, 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</span> </div> </div> <div data-color-mode="auto" data-light-theme="light_tritanopia" class="d-flex flex-column flex-md-row text-mono color-fg-muted"> <time datetime="2023-10-27" class="d-block"> October 27, 2023 </time> <span class="d-none d-md-block mx-3">|</span> <time datetime="2024-02-07" class="d-block mt-1 mt-md-0"> Updated February 7, 2024 </time> <div class="d-flex flex-items-center mt-2 mt-md-0"> <span class="d-none d-md-block mx-3">|</span> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16" class="mr-2"> <path fill="currentcolor" d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm7-3.25v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5a.75.75 0 0 1 1.5 0Z"></path> </svg> 7 minutes </div> </div> </div> <ul class="list-style-none d-flex flex-items-center"> <li> <span class="text-semibold"> Share: </span> </li> <li class="ml-3"> <a 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post type-post status-publish format-standard has-post-thumbnail hentry category-ai-and-ml category-llms tag-ai tag-ai-insights tag-generative-ai tag-github-copilot tag-llm"> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"> <html><body><p>Large language models (LLMs) are revolutionizing the way we interact with software by combining deep learning techniques with powerful computational resources.</p> <p>While this technology is exciting, many are also concerned about how LLMs can generate false, outdated, or problematic information, and how they sometimes even hallucinate (generating information that doesn’t exist) so convincingly. Thankfully, we can immediately put one rumor to rest. According to <a href="https://github.com/whatsinfinitum">Alireza Goudarzi</a>, senior researcher of machine learning (ML) for GitHub Copilot: “LLMs are not trained to reason. They’re not trying to understand science, literature, code, or anything else. They’re simply trained to predict the next token in the text.”</p> <p>Let’s dive into how LLMs come to do the unexpected, and why. This blog post will provide comprehensive insights into LLMs, including their training methods and ethical considerations. Our goal is to help you gain a better understanding of LLM capabilities and how they’ve learned to master language, seemingly, without reasoning.</p> <h2 id="what-are-large-language-models">What are large language models?<a href="#what-are-large-language-models" class="heading-link pl-2 text-italic text-bold" aria-label="What are large language models?"></a></h2> <p>LLMs are AI systems that are trained on massive amounts of text data, allowing them to generate human-like responses and understand natural language in a way that traditional ML models can’t.</p> <p>“These models use advanced techniques from the field of deep learning, which involves training deep neural networks with many layers to learn complex patterns and relationships,” explains <a href="https://github.com/jnbrymn">John Berryman</a>, a senior researcher of ML on the <a href="https://github.com/features/copilot">GitHub Copilot</a> team.</p> <p>What sets LLMs apart is their proficiency at generalizing and understanding context. They’re not limited to pre-defined rules or patterns, but instead learn from large amounts of data to develop their own understanding of language. This allows them to generate coherent and contextually appropriate responses to a wide range of prompts and queries.</p> <p>And while LLMs can be incredibly powerful and flexible tools because of this, the ML methods used to train them, and the quality—or limitations—of their training data, can also lead to occasional lapses in generating accurate, useful, and trustworthy information.</p> <h2 id="deep-learning">Deep learning<a href="#deep-learning" class="heading-link pl-2 text-italic text-bold" aria-label="Deep learning"></a></h2> <p>The advent of modern ML practices, such as deep learning, has been a game-changer when it comes to unlocking the potential of LLMs. Unlike the earliest language models that relied on predefined rules and patterns, deep learning allows these models to create natural language outputs in a more human-like way.</p> <p>“The entire discipline of deep learning and neural networks—which underlies all of this—is ‘how simple can we make the rule and get as close to the behavior of a human brain as possible?’” says Goudarzi.</p> <p>By using neural networks with many layers, deep learning enables LLMs to analyze and learn complex patterns and relationships in language data. This means that these models can generate coherent and contextually appropriate responses, even in the face of complex sentence structures, idiomatic expressions, and subtle nuances in language.</p> <p>While the initial pre-training equips LLMs with a broad language understanding, fine-tuning is where they become versatile and adaptable. “When developers want these models to perform specific tasks, they provide task descriptions and examples (few-shot learning) or task descriptions alone (zero-shot learning). The model then fine-tunes its pre-trained weights based on this information,” says Goudarzi. This process helps it adapt to the specific task while retaining the knowledge it gained from its extensive pre-training.</p> <p>But even with deep learning’s multiple layers and attention mechanisms enabling LLMs to generate human-like text, it can also lead to overgeneralization, where the model produces responses that may not be contextually accurate or up to date.</p> <h2 id="why-llms-arent-always-right">Why LLMs aren’t always right<a href="#why-llms-arent-always-right" class="heading-link pl-2 text-italic text-bold" aria-label="Why LLMs aren’t always right"></a></h2> <p>There are several factors that shed light on why tools built on LLMs may be inaccurate at times, even while sounding quite convincing.</p> <h3 id="limited-knowledge-and-outdated-information">Limited knowledge and outdated information<a href="#limited-knowledge-and-outdated-information" class="heading-link pl-2 text-italic text-bold" aria-label="Limited knowledge and outdated information"></a></h3> <p>LLMs often lack an understanding of the external world or real-time context. They rely solely on the text they’ve been trained on, and they don’t possess an inherent awareness of the world’s current state. “Typically this whole training process takes a long time, and it’s not uncommon for the training data to be two years out of date for any given LLM,” says <a href="https://github.com/wunderalbert">Albert Ziegler</a>, principal researcher and member of the <a href="https://githubnext.com/">GitHub Next</a> research and development team.</p> <p>This limitation means they may generate inaccurate information based on outdated assumptions, since they can’t verify facts or events in real-time. If there have been developments or changes in a particular field or topic after they have been trained, LLMs may not be aware of them and may provide outdated information. This is why it’s still important to fact check any responses you receive from an LLM, regardless of how fact-based it may seem.</p> <h3 id="lack-of-context">Lack of context<a href="#lack-of-context" class="heading-link pl-2 text-italic text-bold" aria-label="Lack of context"></a></h3> <p>One of the primary reasons LLMs sometimes provide incorrect information is the lack of context. These models rely heavily on the information given in the input text, and if the input is ambiguous or lacks detail, the model may make assumptions that can lead to inaccurate responses.</p> <h3 id="training-data-biases-and-limitations">Training data biases and limitations<a href="#training-data-biases-and-limitations" class="heading-link pl-2 text-italic text-bold" aria-label="Training data biases and limitations"></a></h3> <p>LLMs are exposed to massive unlabelled data sets of text during pre-training that are diverse and representative of the language the model should understand. Common sources of data include books, articles, websites—even social media posts!</p> <p>Because of this, they may inadvertently produce responses that reflect these biases or incorrect information present in their training data. This is especially concerning when it comes to sensitive or controversial topics.</p> <p>“Their biases tend to be worse. And that holds true for machine learning in general, not just for LLMs. What machine learning does is identify patterns, and things like stereotypes can turn into extremely convenient shorthands. They might be patterns that really exist, or in the case of LLMs, patterns that are based on human prejudices that are talked about or implicitly used,” says Ziegler.</p> <p>If a model is trained on a dataset that contains biased or discriminatory language, it may generate responses that are also biased or discriminatory. This can have real-world implications, such as reinforcing harmful stereotypes or discriminatory practices.</p> <h3 id="overconfidence">Overconfidence<a href="#overconfidence" class="heading-link pl-2 text-italic text-bold" aria-label="Overconfidence"></a></h3> <p>LLMs don’t have the ability to assess the correctness of the information they generate. Given their deep learning, they often provide responses with a high degree of confidence, prioritizing generating text that <em>appears</em> sensible and flows smoothly—even when the information is incorrect!</p> <h3 id="hallucinations">Hallucinations<a href="#hallucinations" class="heading-link pl-2 text-italic text-bold" aria-label="Hallucinations"></a></h3> <p>LLMs can sometimes “hallucinate” information due to the way they generate text (via patterns and associations). Sometimes, when they’re faced with incomplete or ambiguous queries, they try to complete them by drawing on these patterns, sometimes generating information that isn’t accurate or factual. Ultimately, hallucinations are not supported by evidence or real-world data.</p> <p>For example, imagine that you ask ChatGPT about a historical issue in the 20th century. Instead, it describes a meeting between two famous historical figures who never actually met!</p> <p>In the context of GitHub Copilot, Ziegler explains that “the typical hallucinations we encounter are when GitHub Copilot starts talking about code that’s not even there. Our mitigation is to make it give enough context to every piece of code it talks about that we can check and verify that it actually exists.”</p> <p>But the GitHub Copilot team is already thinking about how to use hallucinations to their advantage in a “top-down” approach to coding. Imagine that you’re tackling a backlog issue, and you’re looking for GitHub Copilot to give you suggestions. As <a href="https://github.com/johanrosenkilde">Johan Rosenkilde</a>, principal researcher for GitHub Next, explains, “ideally, you’d want it to come up with a sub-division of your complex problem delegated to nicely delineated helper functions, and come up with good names for those helpers. And after suggesting code that calls the (still non-existent) helpers, you’d want it to suggest the implementation of them too!”</p> <p>This approach to hallucination would be like getting the blueprint and the building blocks to solve your coding challenges.</p> <h2 id="ethical-use-and-responsible-advocacy-of-llms">Ethical use and responsible advocacy of LLMs<a href="#ethical-use-and-responsible-advocacy-of-llms" class="heading-link pl-2 text-italic text-bold" aria-label="Ethical use and responsible advocacy of LLMs"></a></h2> <p>It’s important to be aware of the ethical considerations that come along with using LLMs. That being said, while LLMs have the potential to generate false information, <strong>they’re not intentionally fabricating or deceiving</strong>. Instead, these arise from the model’s attempts to generate coherent and contextually relevant text based on the patterns and information it has learned from its training data.</p> <p>The GitHub Copilot team has developed a few tools to help detect harmful content. Goudarzi says “First, we have a duplicate detection filter, which helps us detect matches between generated code and all open source code that we have access to, filtering such suggestions out. Another tool we use is called Responsible AI (RAI), and it’s a classifier that can filter out abusive words. Finally, we also separately filter out known unsafe patterns.”</p> <p>Understanding the deep learning processes behind LLMs can help users grasp their limitations—as well as their positive impact. To navigate these effectively, it’s crucial to verify information from reliable sources, provide clear and specific input, and exercise critical thinking when interpreting LLM-generated responses.</p> <p>As Berryman reminds us, “the engines themselves are amoral. Users can do whatever they want with them and that can run the gamut of moral to immoral, for sure. But by being conscious of these issues and actively working towards ethical practices, we can ensure that LLMs are used in a responsible and beneficial manner.”</p> <p>Developers, researchers, and scientists continuously work to improve the accuracy and reliability of these models, making them increasingly valuable tools for the future. All of us can advocate for the responsible and ethical use of LLMs. That includes promoting transparency and accountability in the development and deployment of these models, as well as taking steps to mitigate biases and stereotypes in our own corners of the internet.</p> </body></html> <section class="my-6 my-md-8 mt-md-0"> <hr class="post-tags-separator border-0 mt-0 mb-2" /> <div class="d-flex flex-items-start"> <h2 class="text-mono f4-mktg py-1 mr-3">Tags:</h2> <ul class="d-flex flex-wrap px-0 my-0 list-style-none post-tags"> <li class="my-0"> <a href="https://github.blog/tag/ai/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> AI </a> </li> <li class="my-0"> <a href="https://github.blog/tag/ai-insights/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> AI Insights </a> </li> <li class="my-0"> <a href="https://github.blog/tag/generative-ai/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> generative AI </a> </li> <li class="my-0"> <a href="https://github.blog/tag/github-copilot/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> GitHub Copilot </a> </li> <li class="my-0"> <a href="https://github.blog/tag/llm/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> LLM </a> </li> </ul> </div> </section> <div class="mt-8 mb-8 mb-md-0"> <h2 class="h5-mktg"> Written by </h2> <div class="gradient-separator mt-3 mb-4"></div> <article class="author-bio mb-5 mb-md-4"> <div class="author-bio__content"> <div class="author-bio__avatar"> <picture> <source srcset="https://avatars.githubusercontent.com/u/94011710?v=4&s=200" width="120" height="120" media="(min-width: 768px)" /> <img class="d-block circle" src="https://avatars.githubusercontent.com/u/94011710?v=4&s=200" alt="Jeimy Ruiz" width="80" height="80" loading="lazy" decoding="async" > </picture> </div> <header class="author-bio__header"> <h3 class="f3-mktg text-bold mb-1"> <a href="https://github.blog/author/ruizjeimy/" class="Link--primary color-fg-default">Jeimy Ruiz</a> </h3> <p class="mb-0 f4 lh-condensed"> <a href="https://github.com/ruizjeimy" target="_blank">@ruizjeimy</a> </p> </header> </div> </article> </div> </section> <aside id="sidebar" class="col-12 col-md-4 col-lg-3 post__sidebar" aria-label="Sidebar"> <section aria-label="Tags"> <ul class="d-none d-md-flex flex-items-start flex-wrap list-style-none mt-4 mt-md-0 mb-6 mb-md-8 post-tags"> <li class="my-0"> <a href="https://github.blog/tag/ai/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> AI </a> </li> <li class="my-0"> <a href="https://github.blog/tag/ai-insights/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> AI Insights </a> </li> <li class="my-0"> <a href="https://github.blog/tag/generative-ai/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> generative AI </a> </li> <li class="my-0"> <a href="https://github.blog/tag/github-copilot/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> GitHub Copilot </a> </li> <li class="my-0"> <a href="https://github.blog/tag/llm/" class="d-block border py-2 px-3 f5 lh-condensed-ultra text-semibold Link--primary" rel="tag"> LLM </a> </li> </ul> </section> <div class="position-sticky top-md-scroll"> <div class="d-none d-md-block"> </div> <section aria-labelledby="ai-related-posts-title" class="mb-7 mb-md-8 mt-7 mt-md-0"><h2 class="h5-mktg aside-heading pb-3 border-bottom color-fg-muted color-border-muted" id="ai-related-posts-title">More on <a href="https://github.blog/tag/ai/" class="Link--primary">AI</a></h2><article class="py-4 d-flex flex-column"><div class="col-12 tease-text"><h3 class="h6-mktg mb-12px"><a 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