arXiv:2606.17910v1 Announce Type: cross Abstract: Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query. However, since each document's score depends solely on the embedding of the query and itself, the retrieval process is oblivious to the content of the entire corpus. Therefore, dense retrieval cannot avoid selecting semantically similar documents from the corpus, which may result in a non-diverse, redundant set
The paper addresses an inherent limitation of current dense retrieval models that are becoming dominant but are prone to redundancy, indicating ongoing refinement in AI search methodologies.
Improving information retrieval diversity is crucial for the utility and trustworthiness of AI-powered search, recommendation systems, and large language models, impacting user satisfaction and decision-making.
This research proposes a method to make dense retrieval less redundant and more diverse, potentially leading to more effective and comprehensive search results.
- · Information retrieval systems
- · AI-powered search engines
- · Users of search/recommendation systems
- · Researchers in AI/ML
- · Systems relying on undiversified dense retrieval
- · Inefficient AI search models
Search results become more diverse and less redundant, displaying a wider range of relevant documents.
Improved search quality could enhance the performance of downstream AI applications and user decision-making across various fields.
More nuanced and less biased information access could subtly shift public discourse and knowledge acquisition over time.
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Read at arXiv cs.CL