Examples
Worked examples
- Is an instance
A model report listing results from lm-evaluation-harness v0.4.0 across MMLU, HellaSwag, ARC-c, TruthfulQA.
- Is an instance
A new domain-specific evaluation suite covering 14 medical-coding benchmarks.
Counter-examples
Looks similar, but isn't
- Not an instance
A single accuracy figure without specification of suite or template.
- Not an instance
A leaderboard with undisclosed methodology.
Editorial commentary
A model evaluation suite is the software framework that runs one or more benchmarks against a model under a defined, reproducible configuration — it is the tooling layer, not the test itself. This is the key distinction from a specific benchmark like an MLCommons benchmark or BIG-bench: a benchmark defines a task and a dataset; an evaluation suite is the harness that loads a model, applies a prompting template, runs the model against one or many benchmarks, and scores the output consistently. The same benchmark run through two different evaluation suites, or the same suite run with two different prompting templates, can produce materially different scores — which is why the suite and its exact configuration are part of what needs disclosing, not just the benchmark name and a headline number.
EleutherAI’s lm-evaluation-harness has become the closest thing to a community-default open implementation, supporting a large and growing library of benchmark tasks behind one consistent interface; Stanford’s HELM is a broader-coverage suite that also standardises reporting across multiple metrics (accuracy, calibration, robustness, fairness, toxicity, efficiency) rather than accuracy alone. Narrower, single-purpose suites (HumanEval-style code-execution harnesses, for example) trade coverage for depth on one capability.
What reproducible reporting requires
- The suite and its version (not just “we used a standard evaluation harness”).
- The specific benchmark(s) run within it.
- The prompting template and decoding configuration (temperature, top-p, number of few-shot examples).
- Whether any post-hoc score adjustment or answer-extraction heuristic was applied.
Several widely cited benchmarks commonly run through these suites are now substantially saturated by frontier models — original BIG-bench and BIG-Bench Hard among them — with harder successor tasks (e.g. BIG-Bench Extra Hard) introduced specifically because the earlier tasks stopped discriminating between top models. A suite reporting only saturated benchmarks is measuring less than it appears to.
References
- Gao et al., ‘A framework for few-shot language model evaluation’ (lm-evaluation-harness, 2021-)
- Liang et al., ‘Holistic Evaluation of Language Models’ (HELM, TMLR 2023)
Also known as
LLM eval suite · evaluation harness
Machine-readable encodings
Use in your systems
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