Principle:Hpcaitech ColossalAI Benchmark Metric Computation

Knowledge Sources	ColossalAI
Domains	Evaluation, NLP
Last Updated	2026-02-09 00:00 GMT

Overview

A multi-metric evaluation framework that computes benchmark-specific accuracy, perplexity, ROUGE, BLEU, and mathematical equivalence metrics from model inference results.

Description

Benchmark Metric Computation processes inference results to produce standardized evaluation metrics. Different benchmarks require different metrics: MMLU uses first-token accuracy, GSM8K uses mathematical equivalence, LongBench uses F1 and ROUGE. The evaluator dispatches to the correct metric computation based on the configuration.

Usage

Use after distributed inference to compute final evaluation scores from saved inference results.

Theoretical Basis

Key metrics computed:

• First-token accuracy: Compare first generated token to expected answer letter (for MCQ)
• Perplexity: $PPL=\exp (-\frac{1}{N}\sum _t\log P(x_t|x_{<t}))$
• ROUGE-L: Longest common subsequence-based similarity
• Math equivalence: Numerical answer extraction and comparison

Related Pages

Implemented By

• Implementation:Hpcaitech_ColossalAI_DatasetEvaluator