Tail-Shape Estimation in LLM Evaluation Is Fragile: A Protocol for Diagnosing False Positives
arXiv:2606.16511v1 Announce Type: new Abstract: Recent work motivates moving large language model (LLM) evaluation from mean-based to tail-aware metrics, including conditional value-at-risk and tail-index estimates of reward-model error. We ask whether the canonical extreme-value-theory tail-index parameter, which isolates how heavy a tail is from how large the tail mass is, adds discriminative information beyond the mean and a standard tail-magnitude statistic in LLM evaluation. We pre-register a protocol covering admissibility, goodness-of-fit, threshold-stability, and effect-size requiremen
The rapid advancement and application of large language models necessitate more robust and reliable evaluation methodologies to prevent misleading performance assessments.
Improved evaluation protocols for LLMs are critical for accurately assessing model capabilities, ensuring safe deployments, and guiding future research and investment in AI.
The focus in LLM evaluation shifts from solely mean-based metrics to tail-aware methods, emphasizing robustness against extreme-value performance issues.
- · AI Safety Researchers
- · LLM Developers
- · Organizations deploying LLMs
- · Developers relying on flawed evaluation
- · Users misled by unreliable LLM metrics
More accurate and transparent performance benchmarks for large language models.
Increased trust in LLM deployments as their failure modes become better understood and mitigated.
Accelerated development of more reliable and robust AI systems due to clearer performance signals.
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Read at arXiv cs.LG