arXiv:2606.17664v1 Announce Type: cross Abstract: Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.g., retrieving documents from 2018-2025 for "Who is the president in 2019?" introduces temporal ambiguity). Existing methods rely on supervised training with explicit timestamps, which are not always feasible. We propose
The proliferation of dense retrievers and large document collections highlight the limitations of current unsupervised methods in handling temporal relevance, necessitating advancements.
This research addresses a critical limitation in unsupervised retrieval, leading to more accurate and reliable information access across large, time-sensitive datasets, particularly in AI agent applications.
Retrieval systems can now more effectively leverage temporal context without the burden of explicit timestamp supervision, enhancing the accuracy of information retrieval for time-sensitive queries.
- · AI Agent Developers
- · Information Retrieval Systems
- · Large Language Models
- · Systems Reliant on Manual Timestamping
- · Inefficient Search Engine Algorithms
Improved temporal accuracy in AI agent information retrieval leads to more relevant and timely responses.
Enhanced capabilities for AI agents to process dynamic, evolving datasets, expanding their utility in real-world applications.
Reduced computational and manual overhead for maintaining temporal accuracy in information systems, accelerating development of sophisticated AI applications.
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Read at arXiv cs.AI