Retrieval-Augmented Chain-of-Thought in Semi-structured Domains
Vaibhav Mavi Abulhair Saparov Chen Zhao
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.