LLM-as-Judge

A LLM-as-Judge is an llm evaluation method that is a model-based evaluation technique where a large language model evaluates llm-generated outputs (based on evaluation criteria and scoring rubrics).

• AKA: LLM Judge, Model-as-Judge, AI Judge, LLM Evaluator, Automated LLM Judge.
• Context:
  • It can typically perform Pairwise Comparisons between llm output alternatives, llm model responses, and llm generation variants.
  • It can typically conduct Direct Scoring Assessments using llm rating scales, llm quality rubrics, and llm evaluation prompts.
  • It can typically evaluate Response Relevance through llm semantic analysis, llm context alignment, and llm query matching.
  • It can typically assess Output Correctness via llm factual verification, llm logical consistency, and llm accuracy checking.
  • It can typically measure Generation Quality through llm fluency scores, llm coherence ratings, and llm style assessments.
  • ...
  • It can often detect Hallucination Patterns using llm factuality checks, llm source grounding, and llm claim verification.
  • It can often identify Safety Violations through llm toxicity detection, llm bias identification, and llm harmful content flagging.
  • It can often provide Explanation Generation with llm reasoning traces, llm score justifications, and llm improvement suggestions.
  • It can often implement Multi-Criteria Evaluations across llm quality dimensions, llm assessment aspects, and llm performance factors.
  • It can often support Domain-Specific Assessments for llm specialized tasks, llm expert domains, and llm professional contexts.
  • ...
  • It can range from being a Simple LLM-as-Judge to being a Complex LLM-as-Judge, depending on its llm judge evaluation complexity.
  • It can range from being a Binary LLM-as-Judge to being a Multi-Class LLM-as-Judge, depending on its llm judge output granularity.
  • It can range from being a Single-Turn LLM-as-Judge to being a Multi-Turn LLM-as-Judge, depending on its llm judge interaction depth.
  • It can range from being a Reference-Free LLM-as-Judge to being a Reference-Based LLM-as-Judge, depending on its llm judge grounding requirement.
  • It can range from being a General LLM-as-Judge to being a Specialized LLM-as-Judge, depending on its llm judge domain focus.
  • ...
  • It can utilize Evaluation Prompt Templates with llm judge instructions, llm scoring guidelines, and llm assessment criteria.
  • It can employ Chain-of-Thought Reasoning for llm judge deliberation, llm evaluation steps, and llm reasoning processes.
  • It can leverage Few-Shot Examples through llm judge demonstrations, llm evaluation samples, and llm scoring examples.
  • It can implement Consistency Checks via llm judge calibration, llm score validation, and llm evaluation reliability.
  • ...
• Example(s):
  • Commercial LLM Judges, such as:
    • GPT-4 as Judge for llm general evaluation with llm high accuracy.
    • Claude as Judge for llm safety-focused evaluation with llm constitutional ai principles.
    • Gemini as Judge for llm multi-modal evaluation with llm vision capability.
  • Open-Source LLM Judges, such as:
    • Llama Judge for llm open evaluation with llm community standards.
    • Mistral Judge for llm efficient evaluation with llm resource optimization.
    • Prometheus Judge for llm specialized evaluation with llm fine-tuned capability.
  • Specialized LLM Judges, such as:
    • Code Judge for llm programming evaluation with llm syntax checking.
    • Medical Judge for llm healthcare evaluation with llm clinical accuracy.
    • Legal Judge for llm law evaluation with llm legal reasoning.
  • Evaluation Framework Judges, such as:
    • G-Eval Judge for llm structured evaluation with llm probabilistic scoring.
    • AlpacaEval Judge for llm instruction following with llm win-rate calculation.
    • MT-Bench Judge for llm multi-turn evaluation with llm conversation assessment.
  • ...
• Counter-Example(s):
  • Human Judge, which uses human evaluators rather than llm automated assessment.
  • Rule-Based Evaluator, which applies deterministic rules rather than llm flexible judgment.
  • Exact Match Scorer, which checks string equality rather than llm semantic evaluation.
  • Statistical Metric, which calculates numerical measures rather than llm qualitative assessment.
• See: LLM Evaluation Method, Large-Scale Language Model (LLM), Pairwise Comparison, Direct Scoring, G-Eval, Evaluation Prompt, LLM Benchmark, Human Evaluation, LLM Safety Metric, LLM Application Evaluation Framework, LLM DevOps Framework.

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