arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search to retrieval-augmented generation and knowledge-intensive reasoning. Beyond retrieving items that are individually similar to a query, many applications require a set of results that is also diverse, complementary, and collectively informative. Balancing similarity and diversity is therefore central to effective retrieval, but remains challenging to optimize in a stable and theoretically grounded
The proliferation of advanced AI systems, particularly those using retrieval-augmented generation and semantic search, necessitates more sophisticated vector retrieval techniques to enhance performance and utility.
Improving vector retrieval with both similarity and diversity is crucial for unlocking more effective and nuanced AI applications in information retrieval, knowledge management, and agentic systems.
This research suggests a pathway to more intelligent and contextually aware AI system outputs by optimizing how information is retrieved rather than just increasing similarity.
- · AI software developers
- · Companies building semantic search engines
- · Retrieval-Augmented Generation (RAG) system providers
- · Knowledge management platforms
- · AI systems relying solely on basic similarity retrieval
- · Users dealing with irrelevant or redundant search results
More accurate and contextually rich results from AI applications across various domains.
Accelerated development of AI 'agents' capable of more sophisticated information synthesis and decision-making.
Enhanced trust and broader adoption of AI systems due to improved reliability and relevance of their outputs.
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Read at arXiv cs.CL