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Pei-Ke Zhu

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Sep 2026

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

Large language models are increasingly used to read earnings calls, investor-relations Q&A, guidance, and disclosure language. In this setting, supervised financial NLP benchmarks can become evidence for vendor selection, deployment approval, and model-risk records. Gold labels, however, do not make a benchmark score a...

Si-Di Chang, Pei-Ke Zhu, Yu-Xiao Chen et al. · 0 citations
#artificial intelligence Review Sep 2026

Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML

This single-workflow forensic case is an existence proof of a failure mode, not an estimate of its prevalence: existing human work supports an exploratory audit of synthetic proposals, but not LLM-judge operating characteristics, clinical validity, corpus prevalence, or robust inter-annotator agreement.

Si-Di Chang, Pei-Ke Zhu · 0 citations
#artificial intelligence Preprint Sep 2026

When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation

The case does not show that guardrails are ineffective; it shows their apparent value is unidentified until the simulated agents and protocol pass these checks, and contributes a construct-validity contract separating incentive validity, protocol isolation, stochastic stability, and welfare accounting.

Pei-Ke Zhu, Si-Di Chang · 2 citations

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