DIVAgent: A Diversified Search Agent that Mimics the Human Search Process

Le résumé fourni par la source

Search result diversification plays a crucial role in addressing query ambiguity and multi-faceted information needs by reducing redundancy across documents. While previous supervised approaches can achieve superior performance, they require costly, large-scale annotated data. In contrast, unsupervised methods are more flexible and training-free but rely on manually designed ranking functions, often leading to suboptimal performance. Inspired by how humans explore diverse information during real-world searching, we propose a diversified search agent DIVAgent to combine the advantages of supervised and unsupervised methods. DIVAgent introduces LLMs as the ''brain'' to reason over complex and diverse search results and delineate human cognitive processes into a workflow tailored for search result diversification. Our search agent first identifies potential user intents and then analyzes the alignment of each document to the intents via an intent-aware module. To guide the generation of diversified document rankings, we design an intent-guided ranker that explicitly links documents to their dominant intents while performing greedy document selection. Experimental results demonstrate that DIVAgent significantly outperforms existing unsupervised baselines and achieves competitive performance with supervised models, highlighting the promise of LLMs for diversified ranking in realistic search scenarios.

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