E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning
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Computer Science > Computation and Language
Title:E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning
Abstract:Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly claim-centric; they assess whether generated statements are supported, but not whether evidence remains traceable through rationale, confidence, and final action. We introduce E2A-Bench, a 969-query benchmark for financial chart reasoning, constructed from 323 HS300 constituents under three input modalities with deterministic OHLCV-derived evidence anchors. E2A-Bench evaluates grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage through UCR, RCI, ECI, and NDR, where NDR measures coverage-aware evidence-to-action reliability rather than realized trading performance. Evaluating 20 VLMs reveals three failures hidden by scalar hallucination scores: the lowest-UCR model ranks near the bottom by NDR due to only 6.4% directional coverage; oracle-aided verification reduces unsupported claims but can collapse coverage; and financial fine-tuning amplifies the BUY:SELL ratio by factors of 4.21 to 4.68 across strict base-fine-tuned pairs. These results show that financial VLM evaluation should trace the full evidence-to-action chain rather than rely on a single hallucination score. Code and data: this https URL
| Comments: | EMNLP Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.14302 [cs.CL] |
| (or arXiv:2609.14302v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.14302
arXiv-issued DOI via DataCite (pending registration)
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