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

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

URL: http://arxiv.org/abs/2408.03732v1
Date: Wed, 7 Aug 2024 12:38:23 GMT
Title: Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks
Authors: Zizhang Chen, Pengyu Hong, Sandeep Madireddy,
Abstract summary: We present a novel Question Rephrasing technique to evaluate the input uncertainty of large language models (LLMs) This technique is integrated with sampling methods that measure the output uncertainty of LLMs, thereby offering a more comprehensive uncertainty assessment.
Score: 4.167519875804914
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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