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A multi-turn benchmark evaluating conversational and reasoning abilities of language models using structured prompts and LLM judges.
DESCRIPTION
MTBench is a curated, multi-turn question set designed to assess instruction-following and conversational reasoning. It uses an LLM-as-judge framework to score model responses consistently. The benchmark stresses multi-step reasoning, tool-free dialogue, and diverse question types. It complements human evaluation and single-turn tests. Results correlate with Chatbot Arena but offer reproducibility and controlled prompts. Commonly paired with FastChat for evaluation workflows.
ALTERNATIVE DEFINITIONS
arXiv
We propose MT-Bench, a challenging multi-turn question set for evaluating large language models, and a framework that uses GPT-4 as a judge to evaluate model responses.
Introduces multi-turn prompts and an LLM judge to assess conversational ability.
LMSYS
MT-Bench is a set of carefully curated multi-turn questions and an evaluation pipeline for assessing LLMs’ instruction-following and dialogue capabilities.
Emphasizes evaluation pipeline and instruction-following aspects.
RELATED TERMS
Vicuna GPT-4 Judge LLM Human Evaluation Instruction Tuning Benchmarking FastChat Elo Rating
COMPETING TERMS
KNOWLEDGE AREAS
AI Testing & Validation Data Science MLOps
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Chief AI Officer
EXTERNAL SOURCES
TOP QUESTIONS
What does MTBench measure that single-turn benchmarks miss?
It evaluates sustained instruction-following and reasoning across multiple turns, revealing coherence, consistency, and context retention.
Category: Explanation
How are MTBench responses scored?
An LLM judge such as GPT-4 compares responses against reference criteria. This reduces human effort while maintaining consistency.
Category: Testing
Is MTBench enough to choose a model?
It’s a strong signal for chat quality, but pair with domain tests, safety checks, and latency/cost profiling for production decisions.
Category: BestPractice
What are common mistakes when using MTBench?
Altering prompt formats without noting changes, mixing harness versions, and overgeneralizing from scores to specialized tasks.
Category: Mistakes
How does MTBench relate to Chatbot Arena?
They correlate on general quality; Arena captures human preference at scale, while MTBench offers controlled, reproducible prompts.
Category: Comparison
Can I customize MTBench for my domain?
Yes, extend the question set and keep evaluation protocols consistent. Document all changes for comparability.
Category: HowTo
What’s a hot topic around MTBench today?
Improving LLM-as-judge reliability and exploring multi-modal or tool-augmented variants are active areas.
Category: Topic
How often should I re-run MTBench?
Re-run when upgrading models, changing prompts, or updating harness versions to maintain reliable comparisons.
Category: BestPractice
Does MTBench detect data contamination?
It’s not designed primarily for contamination detection; use specific robust benchmarks and audits for that.
Category: Limitations