arXiv:2606.05170v1 Announce Type: new Abstract: At matched accuracy, open-weight LLMs differ substantially in the shape of their error severity distribution -- a difference invisible to the scalar error rate. Hallucination benchmarks report a single error count and treat all errors as equivalent, yet a wrong date and a fabricated court ruling differ by orders of magnitude. We introduce Errorquake-10k, a 10,000-query benchmark scoring each response on a continuous 0-4 severity scale across 8 domains and 5 difficulty tiers, and we fit per-model severity distributions for 21 open-weight models. F
The proliferation of open-weight LLMs necessitates a more nuanced evaluation beyond simple accuracy, as their deployment in critical applications increases the potential for severe errors.
This research introduces a framework that differentiates the severity of errors, enabling better model selection for sensitive tasks and highlighting a critical blind spot in current LLM evaluation methods.
The focus of LLM evaluation shifts from scalar error rates to a more granular, heavy-tailed distribution of error severity, impacting model development and deployment strategies.
- · LLM developers focusing on robust error mitigation
- · Enterprises deploying LLMs in high-stakes environments
- · Researchers specializing in model safety and interpretability
- · LLM benchmarks relying solely on aggregate error counts
- · Open-weight LLMs with high rates of severe, catastrophic errors
- · Users unaware of the differential impact of various AI hallucination types
New benchmarks like Errorquake-10k become standard for evaluating the safety and reliability of LLMs.
Model developers prioritize reducing the tail risk of severe errors over marginal improvements in overall accuracy.
Regulatory bodies integrate severity-weighted error analysis into AI safety guidelines and certification processes.
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Read at arXiv cs.LG