Unraveling AI Hallucinations: Why LLMs Lie and How to Tame Them

This article explores the phenomenon of 'hallucination' in large language models (LLMs) like ChatGPT, where the AI generates plausible but factually incorrect responses. It delves into the underlying mechanics that lead to hallucinations and strategies to mitigate them.

Details

The article explains that hallucinations in LLMs are a symptom of the underlying mechanics of how these models work. At their core, LLMs are performing complex calculations based on massive amounts of training data, trying to predict the most probable next word. However, they lack the ability to distinguish between truthfulness and plausibility, often generating confident but factually incorrect responses. This is due to the process of 'lossy compression' where the models compress internet-scale data into their parameters, losing fine details in the process. As a result, the models have 'fuzzy memories' and tend to fill in gaps with their 'imagination' or statistical probability, leading to hallucinations. The article also discusses how rare knowledge is more prone to hallucination, as the model's memory of it is faint, creating a 'next token lottery' and snowball effect. To address hallucinations, the article suggests strategies like improved training data, prompting techniques, and safety checks.