A hierarchical multi-agent LLM framework, in which a coordinating analyzer synthesizes query and item specialist outputs, beats flat, staged, and ensemble LLM relevance judges on five content search datasets.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=
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HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems
A hierarchical multi-agent LLM framework, in which a coordinating analyzer synthesizes query and item specialist outputs, beats flat, staged, and ensemble LLM relevance judges on five content search datasets.