ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
Cluster-based partial dense retrieval fused with sparse text retrieval
4 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
4
Pith papers citing it
9
external citations · external index
citation-role summary
background 1
method 1
citation-polarity summary
fields
cs.IR 4years
2026 4representative citing papers
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
The paper defines a bounded reference architecture for LLM-orchestrated hybrid retrieval in dataset search using BM25, dense embeddings, reciprocal rank fusion, and metadata augmentation with pseudo-queries.
PRISM improves e-commerce search robustness by modeling preference-relevance interactions via preference rectification, LLM-driven semantic anchoring with prototypes, and preference-conditioned evidence routing.
citing papers explorer
-
When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
-
Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
-
A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search
The paper defines a bounded reference architecture for LLM-orchestrated hybrid retrieval in dataset search using BM25, dense embeddings, reciprocal rank fusion, and metadata augmentation with pseudo-queries.
-
PRISM: Refracting the Entangled User Behavior Space for E-Commerce Search
PRISM improves e-commerce search robustness by modeling preference-relevance interactions via preference rectification, LLM-driven semantic anchoring with prototypes, and preference-conditioned evidence routing.