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

Source: [EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering](<https://www.sota2.com/research/paper/6a3b64a8206542251d7c9745>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-25T05:00:21.027Z

Authors: Fengchen Gu, Xiaotian Ren, Zhengyong Jiang, Zhilu Zhang, \\'Angel F. Garc\\'ia-Fern\\'andez, Angelos Stefanidis, Mian Zhou, Huakang Li, Jionglong Su

Published: 2026-06-19T06:55:44.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.23724>)

[PDF](<https://arxiv.org/pdf/2606.23724.pdf>)