AI RESEARCH
Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
arXiv CS.CL
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ArXi:2603.16877v2 Announce Type: replace Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model.