EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
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 re...
| Authors | Fengchen Gu, Xiaotian Ren, Zhengyong Jiang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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