Impact of Cross-Encoder Reranking on Multimodal Retrieval for Text-and-Table Financial Question Answering

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. We conduct systematic evaluation using the FinDER benchmark dataset, comprising 1,500 queries across five experimental groups. Res

Research goal: What is the impact of cross-encoder reranking on retrieval performance when benchmarking multimodal models on text-and-table financial QA datasets?

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