Principle:ContextualAI HALOs LM Eval Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | NLP, Evaluation |
| Last Updated | 2026-02-08 03:00 GMT |
Overview
A standardized evaluation framework that measures model capabilities across multiple NLP benchmarks including reasoning, knowledge, and instruction following.
Description
The LM Evaluation Harness (by EleutherAI) provides a unified interface for evaluating language models on dozens of standard benchmarks. The HALOs framework uses it to evaluate aligned models on a curated set of tasks that cover:
- Reasoning: WinoGrande (commonsense), GSM8K (math, chain-of-thought), BBH (Big Bench Hard, few-shot CoT), ARC Easy/Challenge (science reasoning)
- Knowledge: MMLU (Massive Multitask Language Understanding, 57 subjects)
- Language understanding: HellaSwag (sentence completion)
- Instruction following: IFEval (instruction-following evaluation)
The harness handles prompt formatting, few-shot example construction, tokenization, inference, and metric computation in a standardized way, ensuring fair comparison across models.
Usage
Use LM Eval Harness as part of the model evaluation pipeline to get a comprehensive capability profile beyond instruction-following (which AlpacaEval measures). Run after training any alignment method to assess whether the model retains or improves on base capabilities.
Theoretical Basis
Each benchmark task defines:
- A prompt format (including few-shot examples)
- A metric (accuracy, exact match, etc.)
- A scoring method (loglikelihood, generation, multiple choice)
The overall model quality is summarized as the average across all task metrics, providing a single number for comparison. Standard errors are computed via bootstrap to quantify uncertainty.