title: When an LLM is apprehensive about its answers -- and when its
    uncertainty is justified
summary: Uncertainty estimation is crucial for evaluating Large Language Models
  (LLMs), particularly in high-stakes domains where incorrect answers result in
  significant consequences. Numerous approaches consider this problem, while
  focusing on a specific type of uncertainty, ignoring others. We investigate
  what estimates, specifically token-wise entropy and model-as-judge (MASJ),
  would work for multiple-choice question-answering tasks for different question
  topics. Our experiments consider three LLMs: Phi-4, Mistral, and Qwen of
  different sizes from 1.5B to 72B and 14 topics. While MASJ performs similarly
  to a random error predictor, the response entropy predicts model error in
  knowledge-dependent domains and serves as an effective indicator of question
  difficulty: for biology ROC AUC is 0.73. This correlation vanishes for the
  reasoning-dependent domain: for math questions ROC-AUC is 0.55. More
  principally, we found out that the entropy measure required a reasoning amount.
  Thus, data-uncertainty related entropy should be integrated within uncertainty
  estimates frameworks, while MASJ requires refinement. Moreover, existing
  MMLU-Pro samples are biased, and should balance required amount of reasoning
  for different subdomains to provide a more fair assessment of LLMs performance.
paper_authors._id: 67c6e6735aea9d8918635ac2
paper_authors.name: Petr Sychev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T12:01:33.230Z
paper_authors._id: 6728224623d75cbd1cdbe568
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/4sb6TjuzeDc8-PG9hYhjW.jpeg
paper_authors.fullname: Petr Sychev
paper_authors.type: user
paper_authors.user: sspetya
paper_authors._id: 67c6e6735aea9d8918635ac3
paper_authors.name: Andrey Goncharov
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-04T11:39:33.550Z
paper_authors._id: 675708985b91dea24c3ef642
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/675708985b91dea24c3ef642/8KmerI1LwJEBHM2vrC54d.jpeg
paper_authors.fullname: Andrey Goncharov
paper_authors.type: user
paper_authors.user: aigoncharov
paper_authors._id: 67c6e6735aea9d8918635ac4
paper_authors.name: Daniil Vyazhev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T12:01:34.869Z
paper_authors._id: 659e049c01805191e5f67b12
paper_authors.avatarUrl: /avatars/4f33e39d85f8fbdfaeb34143e5038b92.svg
paper_authors.fullname: Vyazhev
paper_authors.type: user
paper_authors.user: DanielVyazhev
paper_authors._id: 67c6e6735aea9d8918635ac5
paper_authors.name: Edvard Khalafyan
paper_authors._id: 67c6e6735aea9d8918635ac6
paper_authors.name: Alexey Zaytsev
paper_title: When an LLM is apprehensive about its answers -- and when its
    uncertainty is justified
paper_summary: Uncertainty estimation is crucial for evaluating Large Language Models
  (LLMs), particularly in high-stakes domains where incorrect answers result in
  significant consequences. Numerous approaches consider this problem, while
  focusing on a specific type of uncertainty, ignoring others. We investigate
  what estimates, specifically token-wise entropy and model-as-judge (MASJ),
  would work for multiple-choice question-answering tasks for different question
  topics. Our experiments consider three LLMs: Phi-4, Mistral, and Qwen of
  different sizes from 1.5B to 72B and 14 topics. While MASJ performs similarly
  to a random error predictor, the response entropy predicts model error in
  knowledge-dependent domains and serves as an effective indicator of question
  difficulty: for biology ROC AUC is 0.73. This correlation vanishes for the
  reasoning-dependent domain: for math questions ROC-AUC is 0.55. More
  principally, we found out that the entropy measure required a reasoning amount.
  Thus, data-uncertainty related entropy should be integrated within uncertainty
  estimates frameworks, while MASJ requires refinement. Moreover, existing
  MMLU-Pro samples are biased, and should balance required amount of reasoning
  for different subdomains to provide a more fair assessment of LLMs performance.
paper_ai_keywords: token-wise entropy model-as-judge (MASJ) multiple-choice question-answering tasks knowledge-dependent domains question difficulty ROC AUC reasoning-dependent domain entropy measure data-uncertainty uncertainty estimates frameworks MMLU-Pro subdomains