Conversation and Chatbot Benchmarks
Conversation quality benchmarks evaluate LLMs’ ability to engage in meaningful, coherent, and helpful dialogues. These benchmarks test conversational skills, context understanding, and response appropriateness across various interaction scenarios.
Overview
These benchmarks assess how well LLMs can:
Maintain coherent conversation flow
Understand and respond to context
Provide helpful and relevant responses
Handle multi-turn conversations
Adapt responses to user needs
Maintain appropriate conversation tone
Key Benchmarks
Chatbot Arena
Purpose: Evaluates conversational quality through human preference judgments
Description: Chatbot Arena uses crowdsourced human evaluations to compare different LLMs in conversational scenarios. Users rate responses based on helpfulness, harmlessness, and overall quality, creating a preference-based ranking system.
Resources: Chatbot Arena | Chatbot Arena Paper
MT-Bench
Purpose: Tests multi-turn conversation capabilities and context retention
Description: MT-Bench evaluates an LLM’s ability to maintain context and coherence across multiple conversation turns. The benchmark tests how well models can follow conversation threads and provide consistent responses.
Resources: MT-Bench dataset
Conversation quality is also evaluated in other benchmarks such as BigBench, which includes dialogue and conversational tasks as part of its comprehensive evaluation framework.