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arXiv · 2606.23724

EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering

Fengchen Gu·Xiaotian Ren·Zhengyong Jiang·Zhilu Zhang·Ángel F. García-Fernández·Angelos Stefanidis·Mian Zhou·Huakang Li·Jionglong Su
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Abstract

Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes financial workflows. A fluent answer can blend directly grounded statements, weak synthesis, and unsupported claims across narrative text, tables, and charts. We present EvidenceLens, a visual analytics prototype that treats financial question answering as a claim-evidence alignment problem. The system decomposes an answer into atomic claims, summarizes support composition and confidence, support gaps, and coordinates claim-level inspection with source passages, table cells, and chart regions. Its core visual representation is a multimodal claim-evidence matrix that makes coverage, contradiction, and modality imbalance immediately visible. To support reproducibility, we also specify a JSON-based artifact schema, a lightweight multimodal alignment pipeline, and a deterministic review-priority ranking that maps backend signals into an auditable visual structure. Through representative report-auditing scenarios, we show how EvidenceLens helps analysts distinguish grounded claims from overconfident synthesis that conventional chat interfaces flatten.

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Fengchen Gu, Xiaotian Ren, Zhengyong Jiang, Zhilu Zhang, Ángel F. García-Fernández, Angelos Stefanidis, Mian Zhou, Huakang Li, Jionglong Su. 2026-06-19. EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering. https://arxiv.org/abs/2606.23724

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