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Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks

arXiv 2408.03732 ↗First posted 7 Aug 2024cs.CL cs.LG q-bio.QMcited by 3

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Abstract (arXiv)

Uncertainty quantification enables users to assess the reliability of responses generated by large language models (LLMs). We present a novel Question Rephrasing technique to evaluate the input uncertainty of LLMs, which refers to the uncertainty arising from equivalent variations of the inputs provided to LLMs. This technique is integrated with sampling methods that measure the output uncertainty of LLMs, thereby offering a more comprehensive uncertainty assessment. We validated our approach on property prediction and reaction prediction for molecular chemistry tasks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 7 Aug 2024.

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21 of 41 references.

References · 21

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9 more
  1. 2017Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Networkcited by 102
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Modelscited by 6
  2. 2025EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistantscited by 5
  3. 2025Beyond statistical significance: Quantifying uncertainty and statistical variability in multilingual and multitask NLP evaluationcited by 0