半结构化领域中的检索增强思维链
Retrieval-Augmented Chain-of-Thought in Semi-structured Domains
AI总结：
本研究针对法律和金融领域问答中LLMs处理长上下文的局限，利用数据的半结构化特性高效检索相关上下文，构建的系统优于当代模型并提供有用解释，推动了LLMs在专业领域的应用。
摘要：
将现有问答（QA）系统应用于法律和金融等专业领域面临挑战，需要领域专业知识。尽管大型语言模型（LLMs）展现出令人印象深刻的语言理解和上下文学习能力，但其无法处理极长输入/上下文的局限性众所周知。这些领域的特定任务需要大量背景知识，导致上下文经常超出LLMs能处理的最大长度。本研究探索利用法律和金融数据的半结构化特性，高效检索相关上下文，从而在领域专用QA中使用LLMs。由此产生的系统优于当代模型，并为答案提供有用解释，鼓励未来研究将LLMs整合到法律和金融NLP系统中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
