The Factuality of Large Language Models in the Legal Domain

Rajaa El Hamdani, Thomas Bonald, Fragkiskos Malliaros, Nils Holzenberger, Fabian Suchanek
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Abstract

This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in the answer, and let the model abstain from answering when uncertain. First, we design a dataset of diverse factual questions about case law and legislation. We then use the dataset to evaluate several LLMs under different evaluation methods, including exact, alias, and fuzzy matching. Our results show that the performance improves significantly under the alias and fuzzy matching methods. Further, we explore the impact of abstaining and in-context examples, finding that both strategies enhance precision. Finally, we demonstrate that additional pre-training on legal documents, as seen with SaulLM, further improves factual precision from 63% to 81%.
法律领域大型语言模型的事实性
本文在现实使用场景中研究了法律领域大型语言模型(LLMs)知识库的事实性问题:我们允许答案有可接受的变化,并让模型在不确定时放弃回答。首先,我们设计了一个关于判例法和立法的各种事实问题的数据集。然后,我们使用该数据集评估了不同评估方法下的几种 LLM,包括精确匹配、别名匹配和模糊匹配。结果表明,在别名匹配和模糊匹配方法下,LLM 的性能有了显著提高。此外,我们还探讨了保留示例和上下文示例的影响,发现这两种策略都能提高精确度。最后,我们证明了在 SaulLM 的基础上对法律文件进行额外的预训练可以进一步提高事实精确度,从 63% 提高到 81%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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