Principle:Togethercomputer Together python LLM Evaluation
| Knowledge Sources | |
|---|---|
| Domains | Evaluation, LLM |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
Principle for evaluating LLM outputs using judge models that classify, score, or compare responses against defined criteria.
Description
LLM Evaluation uses an LLM-as-a-judge paradigm where a separate judge model evaluates the quality of target model outputs. Three evaluation modes are supported: classify (assign categorical labels like "helpful"/"unhelpful"), score (assign numeric scores with pass/fail thresholds), and compare (pairwise comparison of two models). The judge model can be serverless, a dedicated endpoint, or an external API.
Usage
Apply this principle when you need systematic, automated evaluation of LLM outputs at scale. This is appropriate for model selection (comparing candidates), quality monitoring (scoring production outputs), or content moderation (classifying responses). Choose classify for categorical judgments, score for graded assessments, and compare for A/B model testing.
Theoretical Basis
LLM-as-a-judge evaluation follows a structured pipeline:
Pseudo-code Logic:
# Abstract evaluation pipeline
judge = configure_judge(model, system_template)
input_data = load_data(file_path)
if mode == "classify":
results = judge.classify(input_data, labels, pass_labels)
elif mode == "score":
results = judge.score(input_data, min_score, max_score, threshold)
elif mode == "compare":
results = judge.compare(input_data, model_a_outputs, model_b_outputs)
# Aggregate results
pass_rate = count(results.passed) / count(results.total)
Key considerations:
- Judge Selection: Choose a capable judge model; stronger models yield more reliable evaluations
- Template Design: The system template critically affects evaluation quality
- Mode Selection: Classify for binary/multi-class, score for graded, compare for head-to-head
- Data Format: Input data file must contain the fields referenced by model configurations