We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in
Hacker News 1:15 pm on June 10, 2024
- The paper explores uncertainty quantification in large language models (LLMs) to identify when a model's output is unreliable, with an info-theoretic metric. It differentiates between epistemic and aleatoric uncertainties and demonstrates detection of hallucinations through iterative prompting.
- Authors: Yasin Abbasi Yadkor et al.
- Publication type: arXiv preprint
- References & Citations: Acknowledgements, DataCite DOI, Semantic Scholar entries
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https://arxiv.org/abs/2406.02543
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