大型语言模型中的事实性研究

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Qipeng Guo, Xiangkun Hu, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Xuming Hu, Zehan Qi, Wenyang Gao, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang
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引用次数: 0

摘要

这项调查解决了大型语言模型(llm)中事实性的关键问题。随着法学硕士在不同领域的应用,其输出的可靠性和准确性变得至关重要。我们将“事实性问题”定义为法学硕士产生与既定事实不一致的内容的概率。我们首先深入研究这些不准确的含义。随后,我们分析了法学硕士存储和处理事实的机制,寻求事实错误的主要原因。然后我们的讨论转向评估法学硕士真实性的方法,强调关键指标、基准和研究。我们进一步探讨了提高法学硕士真实性的策略。我们的调查为旨在加强法学硕士事实可靠性的研究人员提供了一个结构化的指南。我们一直在https://github.com/wangcunxiang/LLM-Factuality-Survey维护和更新相关的开源材料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey on Factuality in Large Language Models
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the “factuality issue” as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs. We consistently maintain and update the related open-source materials at https://github.com/wangcunxiang/LLM-Factuality-Survey.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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