Related papers: A Survey of Confidence Estimation and Calibration in Large Language Models

A Survey of Confidence Estimation and Calibration in Large Language Models

URL: http://arxiv.org/abs/2311.08298v2
Date: Mon, 25 Mar 2024 06:01:49 GMT
Title: A Survey of Confidence Estimation and Calibration in Large Language Models
Authors: Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, Iryna Gurevych,
Abstract summary: Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
Score: 86.692994151323
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations. There has been a lot of recent research aiming to address this, but there has been no comprehensive overview to organize it and outline the main lessons learned. The present survey aims to bridge this gap. In particular, we outline the challenges and we summarize recent technical advancements for LLM confidence estimation and calibration. We further discuss their applications and suggest promising directions for future work.

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