Researchers have developed TPOUR, a novel training method for unsupervised dense retrievers that addresses the challenge of temporal relevance in document collections spanning multiple time periods. The Temporal Retrieval Preference Optimization (TRPO) technique guides retrievers to favor temporally aligned documents, even without explicit timestamps. TPOUR demonstrates significant improvements over existing unsupervised and supervised baselines, achieving a notable increase in nDCG@5 compared to the much larger Qwen-Embedding-8B model, despite being substantially smaller. AI
IMPACT Improves document retrieval accuracy by incorporating temporal relevance, crucial for time-sensitive information.
RANK_REASON The cluster contains a research paper detailing a new method for unsupervised retrieval.
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Qwen-Embedding-8B
- ScienceCast
- Temporal Retrieval Preference Optimization
- TPOUR
- TPOUR Contriever
- Trust Region Policy Optimization
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