llm-evaluation-provides-stable-capability-assessment

OUT derived (depth 1)

Created 2026-06-21T10:06:23+00:00

LLM evaluation through diverse benchmarking frameworks (MMLU, HLE, HELM, LMArena) provides stable, convergent capability assessments that reliably rank models and track progress.

Justifications

SL — Evaluation stability is gated by two independent undermining factors: metric choice can make emergent abilities appear or vanish, and prompt formatting shifts accuracy by 40%+, meaning benchmark rankings may not reflect stable underlying capabilities

Antecedents (all must be IN):

IN llm-benchmarks-mmlu-hle-lmarena — Key LLM benchmarks and evaluation methods include MMLU, Humanity's Last Exam, LMArena, LLM-as-a-Judge, and perplexity.
IN helm-multi-metric-benchmark — HELM (Holistic Evaluation of Language Models) from Stanford CRFM evaluates models across accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency

Unless (any of these IN defeats this justification):

IN emergent-abilities-metric-artifact-debate — The appearance of emergent abilities in LLMs depends on metric choice: accuracy metrics show step-function discontinuities while log-probability metrics show smooth scaling curves (Schaeffer et al.).
IN prompt-sensitivity-40-percent-accuracy-shift — LLM performance is highly sensitive to prompt design, with accuracy shifts of over 40 percentage points from minor changes such as reordering examples, and up to 76 accuracy points difference across formatting changes