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Evidence record 5185 · automatically gathered

Measurement Risk in Supervised Financial NLP: Rubric and Metric Sensitivity on JF-ICR

As LLMs become credible readers of earnings calls, investor-relations Q\&A, guidance, and disclosure language, supervised financial NLP benchmarks increasingly function as decision evidence for model selection and deployment. A hidden assumption is that gold labels make such evidence objective. This assumption breaks down when the benchmark ruler itself is sensitive to rubric wording, metric choice, or aggregation policy. We study this measurement risk on Japanese Financial Implicit-Commitment R

Record details

Published: 30 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Transparency · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (30 April 2026), “Measurement Risk in Supervised Financial NLP: Rubric and Metric Sensitivity on JF-ICR,” evidence record 5185, https://ethics.ai/record/5185 (originally published by arXiv).

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