Episode description

In this episode, we delve into the intriguing challenge of "hallucinations" in large language models (LLMs)—responses that are grammatically correct but factually incorrect or nonsensical. Drawing from a groundbreaking paper, we explore the concept of epistemic uncertainty, which stems from a model's limited knowledge base.

Unlike previous approaches that often only measure the overall uncertainty of a response, the authors introduce a new metric that distinguishes between epistemic and aleatoric (random) uncertainties. This distinction is crucial for questions with multiple valid answers, where high overall uncertainty doesn't necessarily indicate a hallucination.

Experimentally, the authors demonstrate that their method outperforms existing approaches, especially in datasets that include both single-answer and multiple-answer questions. Their method is particularly effective in high-entropy questions, where the model is uncertain about the correct answer.

Join us as we unpack this promising approach to detecting hallucinations in LLMs, grounded in solid theoretical foundations and proven effective in practice.

This episode is based on the paper: Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári. "To Believe or Not to Believe Your LLM", ArXiv:2406.02543v1, 2024, it can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.