HELM
A comprehensive evaluation framework for large language models published by Scale AI. It covers multiple dimensions including standardized tests, safety, and fairness, with the goal of providing a holistic assessment of language model capabilities across a broad range of metrics.
Timeline (last 90 days)
There is often a gap between vendors' marketing 'benchmarking' claims and actual third-party evaluation results; the marketing may be based on internally selected benchmarks or specific tasks, while independent evaluations (such as LMSYS Chatbot Arena, HELM, etc.) often present a more complex picture.
Leaderboard operators such as HELM and BIG-bench maintain private holdout sets that have never been publicly released and are only unlocked after each evaluation, in order to guard against leakage.
Academia is exploring dynamic benchmarks (such as HELM and LiveBench) and adversarial evaluation (Adversarial NLI) as alternative approaches.
Around 2023, multiple academic benchmarks including BIG-bench and HELM were pointed out to have test set contamination issues, meaning the models' training data may have already included evaluation questions, leading to inflated scores.
HELM (Holistic Evaluation of Language Models) is a comprehensive academic evaluation suite that attempts to compare large model performance across multiple dimensions.
Commonly used comprehensive evaluation frameworks include MMLU, HumanEval, MT-Bench, and HELM, each focusing on different capability dimensions.
The article states that the HELM evaluation framework was released by Scale AI.
HELM (Stanford) comprehensively evaluates models across multiple dimensions including accuracy, calibration, and robustness.
All Facts (12)
Percy Liang主导了HELM（Holistic Evaluation of Language Models）项目，这是一个对大语言模型进行全面、标准化评估的基准框架
95%UnverifiedHELM的7大评估指标包括准确性、校准性、鲁棒性、公平性、偏见与刻板印象、毒性以及效率
90%UnverifiedHELM框架设计了涵盖42个核心场景和7大评估指标的多维度评估体系
90%UnverifiedHELM的后续版本已扩展到对视觉语言模型和代码生成模型的评估
85%UnverifiedThere is often a gap between vendors' marketing 'benchmarking' claims and actual third-party evaluation results; the marketing may be based on internally selected benchmarks or specific tasks, while independent evaluations (such as LMSYS Chatbot Arena, HELM, etc.) often present a more complex picture.
50%UnverifiedLeaderboard operators such as HELM and BIG-bench maintain private holdout sets that have never been publicly released and are only unlocked after each evaluation, in order to guard against leakage.
50%UnverifiedAcademia is exploring dynamic benchmarks (such as HELM and LiveBench) and adversarial evaluation (Adversarial NLI) as alternative approaches.
50%UnverifiedAround 2023, multiple academic benchmarks including BIG-bench and HELM were pointed out to have test set contamination issues, meaning the models' training data may have already included evaluation questions, leading to inflated scores.
50%UnverifiedHELM (Holistic Evaluation of Language Models) is a comprehensive academic evaluation suite that attempts to compare large model performance across multiple dimensions.
50%UnverifiedCommonly used comprehensive evaluation frameworks include MMLU, HumanEval, MT-Bench, and HELM, each focusing on different capability dimensions.
50%UnverifiedThe article states that the HELM evaluation framework was released by Scale AI.
50%UnverifiedHELM (Stanford) comprehensively evaluates models across multiple dimensions including accuracy, calibration, and robustness.
50%