标题： 质疑案例判断摘要中的偏见：法律数据集还是大型语言模型？ 显示英文标题

标题： Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?

摘要： 法律数据集的演变和大型语言模型(LLMs)的出现极大地改变了法律领域，尤其是在案件判决摘要的生成方面。然而，一个关键的问题是这些摘要中可能存在的偏见。本研究审查了法律数据集和大型语言模型产生的案件判决摘要中的偏见。研究旨在分析偏见对法律决策的影响。通过质疑这些摘要中偏见的准确性、公平性和影响，本研究有助于更好地理解技术在法律环境中的作用以及对全球司法系统的影响。在本研究中，我们调查了与性别相关关键词、与种族相关关键词、针对女性犯罪的相关关键词、国家名称和宗教相关关键词相关的偏见。研究表明，大型语言模型和预训练的抽象摘要模型生成的输出中存在有趣的偏见证据。这些偏见背后的原因需要进一步研究。 显示英文摘要

摘要： The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries. However, a critical concern arises regarding the potential biases embedded within these summaries. This study scrutinizes the biases present in case judgment summaries produced by legal datasets and large language models. The research aims to analyze the impact of biases on legal decision making. By interrogating the accuracy, fairness, and implications of biases in these summaries, this study contributes to a better understanding of the role of technology in legal contexts and the implications for justice systems worldwide. In this study, we investigate biases wrt Gender-related keywords, Race-related keywords, Keywords related to crime against women, Country names and religious keywords. The study shows interesting evidences of biases in the outputs generated by the large language models and pre-trained abstractive summarization models. The reasoning behind these biases needs further studies.