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        <title>Pareto Optimal Learning for Estimating Large Language Model Errors</title>
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    <abstract>Large Language Models (LLMs) have shown impressive abilities in many applications. When a concrete and precise answer is desired, it is important to have a quantitative estimation of the potential error rate. However, this can be challenging due to the text-in-text-out nature of the generative models. We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information. We prove theoretically that the error estimator optimized in our framework aligns with the LLM and the information sources in an Pareto optimal manner. Experimental results show that the risk scores estimated by our method are well correlated with the true LLM error rate, thus facilitating error correction. By dynamically combining with prompting strategies such as self-verification and information retrieval, we demonstrate the proposed method can be utilized to increase the performance of an LLM, surpassing state-of-the-art task specific model.</abstract>
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%0 Conference Proceedings
%T Pareto Optimal Learning for Estimating Large Language Model Errors
%A Zhao, Theodore
%A Wei, Mu
%A Preston, J.
%A Poon, Hoifung
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-etal-2024-pareto
%X Large Language Models (LLMs) have shown impressive abilities in many applications. When a concrete and precise answer is desired, it is important to have a quantitative estimation of the potential error rate. However, this can be challenging due to the text-in-text-out nature of the generative models. We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information. We prove theoretically that the error estimator optimized in our framework aligns with the LLM and the information sources in an Pareto optimal manner. Experimental results show that the risk scores estimated by our method are well correlated with the true LLM error rate, thus facilitating error correction. By dynamically combining with prompting strategies such as self-verification and information retrieval, we demonstrate the proposed method can be utilized to increase the performance of an LLM, surpassing state-of-the-art task specific model.
%U https://aclanthology.org/2024.acl-long.566
%P 10513-10529

Markdown (Informal)

[Pareto Optimal Learning for Estimating Large Language Model Errors](https://aclanthology.org/2024.acl-long.566) (Zhao et al., ACL 2024)

• Pareto Optimal Learning for Estimating Large Language Model Errors (Zhao et al., ACL 2024)

ACL

• Theodore Zhao, Mu Wei, J. Preston, and Hoifung Poon. 2024. Pareto Optimal Learning for Estimating Large Language Model Errors. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10513–10529, Bangkok, Thailand. Association for Computational Linguistics.