Applying existing question answering (QA) systems to specialized domains like
law and finance presents challenges that necessitate domain expertise. Although
large language models (LLMs) have shown impressive language comprehension and
in-context learning capabilities, their inability to handle very long
inputs/contexts is well known. Tasks specific to these domains need significant
background knowledge, leading to contexts that can often exceed the maximum
length that existing LLMs can process. This study explores leveraging the
semi-structured nature of legal and financial data to efficiently retrieve
relevant context, enabling the use of LLMs for domain-specialized QA. The
resulting system outperforms contemporary models and also provides useful
explanations for the answers, encouraging the integration of LLMs into legal
and financial NLP systems for future research.

本研究通过利用法律和金融数据的半结构化特点，实现有效检索相关背景知识来解决将现有问答系统应用于法律和金融等专业领域所面临的挑战，使得大型语言模型在领域特定的问答任务中表现优异，同时提供有用的答案解释，鼓励将大型语言模型整合到未来的法律和金融自然语言处理系统中进行研究。