Fixing Hallucinations in LLMs
<p>Generative Large Language Models (LLMs) can generate highly fluent responses to various user prompts. However, their tendency to hallucinate or make non-factual statements can compromise trust.</p>
<blockquote>
<p>I think we will get the hallucination problem to a much, much better place… it will take us a year and a half, two years. — OpenAI CEO Sam Altman</p>
</blockquote>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*fV5FmGBwGZa6Qo6EruW4Gw.png" style="height:147px; width:700px" /></p>
<p>Is this ChatGPT answer a hallucination?</p>
<p>As developers look to build systems with models, these limitations present a real challenge as the overall system must meet quality, safety, and groundedness requirements. For example, can we trust that an automatic code review provided by an LLM is correct? Or the returned answer to questions on how to handle insurance-related tasks is reliable?</p>
<p>This article begins with an overview of how hallucination remains a persistent challenge with LLMs, followed by steps (and associated research papers) that address hallucination and reliability concerns.</p>
<blockquote>
<p><strong>Disclaimer</strong>: The information in the article is current as of August 2023, but please be aware that changes may occur thereafter.</p>
</blockquote>
<h1>“Short” Summary</h1>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*9mkes5F4L3XEpXs8G6tavw.png" style="height:271px; width:700px" /></p>
<p>Comparison of experimental results</p>
<p>Hallucinations in Large Language Models stem from data compression and inconsistency. Quality assurance is challenging as many datasets might be outdated or unreliable. To mitigate hallucinations:</p>
<p><a href="https://betterprogramming.pub/fixing-hallucinations-in-llms-9ff0fd438e33"><strong>Click Here</strong></a></p>