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LLM Evaluation & Benchmarking — HELM, MMLU, MT-Bench, Arena, LLM-as-Judge
How to evaluate foundation and chat models without fooling yourself — HELM’s multi-metric design, instruction-following suites, chat leaderboards, RAGAS for grounded systems, contamination, and when human eval still wins. Connects public benchmarks to a private canary stack you can ship against.
One Leaderboard Number Never Ships a Product
HELM (Holistic Evaluation of Language Models; Liang et al., TMLR 2023; arXiv:2211.09110) was built to fix a broken habit: every lab reporting a different thin slice of tasks with incomparable conditions. HELM taxonomizes scenarios and metrics, then measures multiple dimensions — accuracy, calibration, robustness, fairness, bias, toxicity, efficiency — so tradeoffs surface instead of hiding behind a cherry-picked accuracy cell.
MMLU (Hendrycks et al.) became the de-facto “general knowledge” bar for base models — and immediately invited contamination (test items appearing in pretraining) and teaching-to-the-test in post-training. MT-Bench and LMSYS Chatbot Arena move closer to assistant UX with pairwise preferences and Elo — closer to what users feel, but with position bias, verbosity bias, and a moving opponent pool.
Staff-level answers decouple academic ranking from your distribution: tools, RAG, JSON contracts, latency, and policy. You own a private canary with redacted prompts and task success definitions — public numbers are ordinal hints, not SLAs.
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