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To Believe or Not to Believe Your LLM
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We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers). In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected. We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest.
Forward citations
Cited by 7 Pith papers
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Reliability Scales Inversely: Hallucinations Snowball Faster in Bigger Language Models
Hallucination in LLMs is driven by an oracle-invisible “decoding risk” term that grows with scale and causally compounds errors within a response.
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An automated LLM-based evaluator finds that eight LLMs rarely hallucinate factors in legal argument generation but often omit relevant factors and usually fail to abstain when no common ground exists.
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IB-EDL regularizes evidential deep learning with an information bottleneck to reduce overconfidence in fine-tuned LLMs, improving calibration across multiple benchmarks.
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Hallucination Detection with Small Language Models
A multi-small-model ensemble with sentence splitting, z-score normalization, and harmonic mean detects hallucinations in RAG answers with a reported 10% F1 gain over single-model baselines.
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Sampling multiple VLM-generated visual descriptions and letting a text-only LLM vote on the diagnosis improves zero-shot medical image classification on three MedMNIST datasets.
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