MTR-Suite offers an LLM-based auditor, a low-cost multi-agent synthesis pipeline using greedy traversal clustering, and a new general-domain benchmark with superior discriminative power for conversational retrieval.
Coral: Benchmarking multi-turn conversational retrieval-augmentation generation
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
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MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks
MTR-Suite offers an LLM-based auditor, a low-cost multi-agent synthesis pipeline using greedy traversal clustering, and a new general-domain benchmark with superior discriminative power for conversational retrieval.
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RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
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