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Found 6 papers, 4 papers with code
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
The dual-encoder has become the de facto architecture for dense retrieval.
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model.
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.
PROD: Progressive Distillation for Dense Retrieval
It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student.
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways.
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