Holistic Evaluation of Language Models (HELM) Benchmarking Task

A Holistic Evaluation of Language Models (HELM) Benchmarking Task is a LLM inference evaluation task that can be used to evaluate language models across multiple dimensions including accuracy, calibration, robustness, fairness, and toxicity.

• AKA: HELM LLM Benchmarking Framework, HELM Benchmark.
• Context:
  • Task Input: Scenario prompt, which could involve summarization, QA, translation, etc.
  • Task Optional Input: Scenario metadata (e.g., task category, domain context)
  • Task Output: Text generation (single or multi-turn)
  • Task Performance Measure/Metrics: Accuracy, Calibration, Fairness, Robustness, Bias, Efficiency.
  • It can be used improve the transparency and understanding of large language models (LLMs) by evaluating them across multiple scenarios and metrics.
  • It can (typically) evaluate Large Language Models across 42 different scenarios, covering tasks such as Question Answering, Summarization, and Toxicity Detection.
  • It can (often) utilize a multi-metric approach to assess not only Accuracy but also Calibration, Robustness, Fairness, Bias, and Efficiency.
  • It can range from evaluating models like GPT-3 to models from organizations such as AI21 Labs, Anthropic, BigScience, and Google.
  • It can facilitate the comparison of Language Models under consistent conditions, ensuring fairness in evaluation.
  • It can highlight trade-offs and performance characteristics of different language models, aiding in their understanding and development.
  • It can support research in Foundation Models by providing comprehensive and standardized benchmarking data.
  • It can inform the deployment of LLMs by evaluating their performance on societal impact metrics.
  • It can include both core scenarios and targeted evaluations to provide a holistic understanding of LLM capabilities and risks.
  • It can support fine-grained and standardized comparison of dozens of foundation models.
  • It can simulate realistic user tasks in domains like summarization, translation, dialogue, and classification.
  • It can range from simple factual QA to multi-turn reasoning tasks with risk assessment.
  • ...
• Example(s):
  • an evaluation of the GPT-3 model on tasks like summarization and question answering, providing insights into its strengths and weaknesses.
  • A benchmarking report comparing the performance of models from different organizations such as Google and OpenAI on fairness and bias metrics.
  • an evauation to compare GPT-3, Claude, and PaLM across the same scenario sets and dimensions.
  • Evaluation of OPT and BLOOM on HELM for open-source transparency in LLM benchmarking.
  • Applying HELM’s JSON-based scenario specification to custom benchmarking pipelines.
  • ...
• Counter-Example(s):
  • Single-Metric Benchmarks such as MMLU or SQuAD, which only evaluate one dimension of performance.
  • Training-Time Evaluation Tasks, which are not concerned with post-training model behavior.
  • Instruction-Following Datasets such as FLAN or Alpaca, which are used to fine-tune models, not evaluate them.
  • GLUE Benchmarking Task,
  • HotpotQA Benchmarking Task,
  • EleutherAI Harness (Hugging Face).
  • ...
• See: Bias Evaluation, Calibration Evaluation, Comprehensive Benchmarking, Foundation Model Evaluation, Large Language Models.

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References

2024a

• (Stanford CRFM, 2024) ⇒ Stanford CRFM. (2024). "Holistic Evaluation of Language Models (HELM)". In: Stanford CRFM.
  • QUOTE: Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.

2024b

• (Stanford CRFM, 2024) ⇒ Stanford CRFM. (2024). "HELM GitHub Repository". In: GitHub.
  • QUOTE: HELM includes the following features: * Datasets and benchmarks in a standardized format (e.g. MMLU-Pro, GPQA, IFEval, WildBench) * Models from various providers accessible through a unified interface (e.g. OpenAI models, Anthropic Claude, Google Gemini) * Metrics for measuring various aspects beyond accuracy (e.g. efficiency, bias, toxicity) * Web UI for inspecting individual prompts and responses * Web leaderboard for comparing results across models and benchmarks

2022

• (Stanford, 2022) ⇒ Stanford University. (2022). "Stanford debuts first AI benchmark to help understand LLMs." In: HAI. [URL](https://hai.stanford.edu/news/stanford-debuts-first-ai-benchmark-help-understand-llms)
  • NOTE: It details the launch and purpose of the HELM project, emphasizing its role in standardizing the evaluation of large language models.
  • QUOTE: HAI’s Center for Research on Foundation Models launches Holistic Evaluation of Language Models (HELM), the first benchmarking project aimed at improving the transparency of language models and the broader category of foundation models.

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