Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
Paper
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2204.00718
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Published
Hang Li
hanglics
AI & ML interests
NLP, LLM, Relevance Feedback, Information Retrieval, ML
Recent Activity
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ielabgroup/dse-qwen2-2b-mrl-v1-safetensor
authored
a paper
9 days ago
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
authored
a paper
9 days ago
Pseudo Relevance Feedback is Enough to Close the Gap Between Small and
Large Dense Retrieval Models
Organizations
authored
11
papers
9 days ago
Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models
Paper
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2503.14887
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Published
LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback
Paper
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2504.01448
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Published
TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
Paper
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2401.13509
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Published
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Paper
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2112.06400
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Published
AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
Paper
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2212.10762
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Published
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
Paper
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2212.09018
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Published
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Paper
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2108.11044
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Published
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Paper
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2205.05888
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Published
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
Paper
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2205.00235
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Published
MeSH Term Suggestion for Systematic Review Literature Search
Paper
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2112.00277
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Published
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a
paper
8 months ago
authored
a
paper
9 months ago