This paper explains that hallucinations in large language models (LLMs) aren’t due to flawed data, but to the way these models are trained and evaluated. LLMs are incentivized to guess rather than admit uncertainty, leading to errors that are statistically predictable. The authors frame this as a binary classification problem – correctly identifying valid outputs – and demonstrate a link between misclassification rate and hallucination rate. They argue that fixing this requires a shift in evaluation metrics, moving away from rewarding overconfidence and towards accepting uncertainty, to build more trustworthy models.
An encyclopedia where everything can be an article, and every article is generated on the spot. Articles are often full of hallucinations and nonsense, especially with lower parameter models. The project uses Ollama and Go to generate content.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
