PAI LLM-as-a-Judge evaluates and scores LLM outputs automatically, providing accurate, efficient, and easy-to-use model evaluation without manual annotation.
Background
Model evaluation verifies LLM performance, guides model selection, optimizes inference calls, and validates service reliability. Common methods include:
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Metric-based evaluation
Calculates similarity between generated and reference text using BLEU, ROUGE, and METEOR. Fast but limited to specific scenarios (summarization, translation), requires reference text, and often misses deeper semantics.
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Benchmark evaluation
Tests models on standardized datasets (MMLU, GSM8k, HumanEval). Results are comparable across models but cannot assess subjective or open-ended responses.
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Human evaluation
Human reviewers score outputs against defined criteria. Captures complex semantics and aligns with human expectations, but is resource-intensive and slow.
LLM-as-a-Judge addresses these limitations with automated batch evaluations for both subjective and objective questions—no manual annotation or task-specific constraints required.
Features
PAI LLM-as-a-Judge generates automated scores from question-answer pairs. The core evaluation workflow:
Key features of LLM-as-a-Judge:
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Accurate: Classifies questions into scenarios (open-ended, creative writing, code generation, role assumption) and applies scenario-specific criteria for higher accuracy.
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Efficient: No manually annotated data required. Input questions and model answers to get automatic scores and analysis.
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Easy to use: Access through the console, API, or SDK—quick onboarding for new users and flexible integration for developers.
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Low cost: Delivers evaluation performance comparable to ChatGPT-4 in Chinese language scenarios at low cost.
Billing
Billing details are covered in Billing for LLM-as-a-Judge.
Get started
After you enable LLM-as-a-Judge, access the service through:
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Use LLM-as-a-Judge directly in the PAI console.
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API call examples, API reference
Call the service online via Python SDK or HTTP, or submit batch data for offline algorithm service calls. Returns evaluation scores and reasoning for each input.
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Run inference and evaluation together on predefined LLM models in the console.