LUQ: Long-text Uncertainty Quantification for LLMs

• URL: http://arxiv.org/abs/2403.20279v3
• Date: Fri, 04 Oct 2024 09:19:07 GMT
• Title: LUQ: Long-text Uncertainty Quantification for LLMs
• Authors: Caiqi Zhang, Fangyu Liu, Marco Basaldella, Nigel Collier,
• Abstract summary: Large Language Models (LLMs) are prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model's confidence on its generation. We propose textscLuq-Ensemble, a method that ensembles responses from multiple models and selects the response with the lowest uncertainty.
• Score: 29.987010627250527
• License:
• Abstract: Large Language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model's confidence on its generation, thereby aiding in the mitigation of nonfactual outputs. Existing research on UQ predominantly targets short text generation, typically yielding brief, word-limited responses. However, real-world applications frequently necessitate much longer responses. Our study first highlights the limitations of current UQ methods in handling long text generation. We then introduce \textsc{Luq} and its two variations, a series of novel sampling-based UQ approaches specifically designed for long text. Our findings reveal that \textsc{Luq} outperforms existing baseline methods in correlating with the model's factuality scores (negative coefficient of -0.85 observed for Gemini Pro). To further improve the factuality of LLM responses, we propose \textsc{Luq-Ensemble}, a method that ensembles responses from multiple models and selects the response with the lowest uncertainty. The ensembling method greatly improves the response factuality upon the best standalone LLM.

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