Temporal Preference Optimization for Unsupervised Retrieval
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 TPOUR (Temporal Preference Optimization for Unsupervised Retriever), which uses our novel training method Temporal Retrieval Preference Optimizati...
| Authors | HyunJin Kim, Jaejun Shim, Young Jin Kim et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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