Article

MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems

2025-01-16

Abstract excerpt

Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on _multi-turn_ RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges. We present mtRAG: an end-to-end human-generated multi-turn RAG benchmar...

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Literature Corpus work: a79fc981-6b0c-588f-8c09-0e0ccd12fa71
DOI: 10.32388/phs6yn

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