The Benchmark — HELM (Holistic Evaluation of Language Models)
A plain-English explainer of one AI evaluation benchmark: what it measures, how it works, and when to trust it.
HELM (Holistic Evaluation of Language Models)
A framework that measures language models across multiple capabilities, datasets, and metrics simultaneously to avoid over-optimizing for single benchmarks.
What it measures
HELM evaluates language models on 16+ diverse tasks spanning language understanding, generation, reasoning, and factuality — including reading comprehension, question answering, summarization, toxicity detection, and bias measurement. Rather than a single score, it produces a detailed report card showing how models perform across different dimensions, with explicit transparency about tradeoffs (e.g., a model might excel at factuality but struggle with fairness).
Why it was created
Released by Stanford’s Center for Research on Foundation Models in November 2022, HELM was designed to address “benchmark saturation” — the observation that practitioners were cherry-picking individual benchmarks to show their model in the best light, and leaderboards didn’t capture the full picture of model capabilities or limitations. The authors wanted a comprehensive evaluation protocol that would be harder to game and would surface real-world tradeoffs.
How it works
HELM runs models against 22 core scenarios (expanding to 50+), each containing 100–2,000+ test instances. For instance, the MMLU scenario uses the full 14,000-question dataset; the ToxiGen scenario measures toxic text generation with 950 prompts. Each scenario is scored on multiple metrics simultaneously: accuracy, BLEU, ROUGE, F1, toxicity scores, and fairness measures. The framework explicitly logs which instances require API calls, how many tokens are consumed, and latency — making it reproducible. Results are presented in a multi-dimensional dashboard rather than a single number.
What scores mean in practice
There is no single “HELM score,” which is intentional. Instead, models are compared on individual scenarios. As of Q1 2024, on MMLU within HELM, GPT-4 scores ~86%, Claude 3 Opus ~85%, and Llama 2 70B ~69%. On the factuality task (TriviaQA), GPT-4 achieves ~85% F1, while smaller models drop to ~55–65%. Crucially, HELM also reports tradeoffs: models maximizing toxicity avoidance sometimes underperform on other metrics. A year ago (2023), open models like Llama scored 10–15 points lower across most tasks.
Known limitations
- Benchmark saturation still applies: Even HELM has become a de facto optimization target. Some labs now tune specifically for HELM scenarios, and it’s unclear if published HELM numbers reflect held-out evaluation or optimization on the test set itself.
- Incomplete real-world coverage: 16–50 scenarios still don’t capture all failure modes. HELM doesn’t include adversarial robustness, long-context reasoning, or multi-turn dialogue depth, which matter in production.
- Metric interpretation issues: Metrics like BLEU and ROUGE are known to correlate poorly with human judgment for generation tasks. High HELM scores don’t guarantee usable outputs; toxicity scores can also be gamed by models that simply refuse to respond.
When to trust it (and when not to)
- Trust it for: Comparative analysis across multiple capabilities at once. If you need to understand whether a model is well-rounded or lopsided (e.g., good at reasoning but poor at factuality), HELM’s multi-metric dashboard is the best public tool available. Use it to spot tradeoffs, not to pick a single “best” model.
- Don’t trust it alone for: Production deployment decisions. HELM gives breadth but limited depth. For critical tasks (e.g., code generation, customer support), you should still run task-specific benchmarks and human evals on your actual use case. A high HELM score on TriviaQA doesn’t guarantee a model won’t hallucinate in your retrieval-augmented system.