Principle:ContextualAI HALOs Metrics Summarization
| Knowledge Sources | |
|---|---|
| Domains | NLP, Evaluation, Data_Engineering |
| Last Updated | 2026-02-08 03:00 GMT |
Overview
An automated extraction and aggregation pipeline that parses evaluation log files into structured metrics tables for cross-model comparison.
Description
After running evaluations (AlpacaEval + LM Eval Harness) on multiple models, the results exist as unstructured log files. Metrics summarization parses these logs using regex patterns, extracts per-task scores, computes an average across all tasks, and outputs structured CSV/JSON tables.
This enables systematic comparison across models and training configurations without manual log reading. The summary includes:
- AlpacaEval metrics: win rate (WR) and length-controlled win rate (LCWR)
- LM Eval Harness metrics: per-task scores (WinoGrande, MMLU, GSM8K, etc.)
- An overall average score across all non-AlpacaEval tasks
Usage
Use after running both AlpacaEval and LM Eval Harness. Point the script at a log file or directory of log files to generate comparison tables.
Theoretical Basis
The aggregation computes a simple arithmetic mean across benchmark scores:
Where is the number of benchmark tasks (excluding AlpacaEval metrics, which use a different scale). This provides a single summary number for ranking models, though individual task scores should be consulted for detailed analysis.