TREC
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
SatIR: Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching
Announce Type: replace Abstract: Many important retrieval problems are not merely problems of semantic similarity, but problems of constraint satisfaction: a retrieved item should be topically relevant to a query and satisfy explicit requirements involving negation, temporal conditions, numeric thresholds, exceptions, ontological relations, and incomplete evidence. We study this challenge in clinical trial matching, a high-stakes test bed where a useful trial must both address a patient's...
RCEM: Embedder Equipped with Query Rewriting Skill for Robust Conversational Search in Distributional Shift
arXiv:2606.01697v1 Announce Type: new Abstract: Conversational search has become increasingly important in retrieval-augmented generation (RAG) systems, where users interact with AI assistants through multi-turn conversations containing context-dependent queries. We propose RCEM, a conversational dense retrieval model that distills the query reformulation capability of LLMs into the embedding model, enabling context-aware retrieval without explicit query rewriting during inference. Unlike...
SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics
Announce Type: new Abstract: Scalable information retrieval testing needs corpora that are large enough to stress index construction, ranking latency, query routing, and evaluation tooling, yet human-judged test collections remain expensive and may be unavailable when documents are private or still under design. This paper introduces SPECTRA, a reproducible framework for generating synthetic text corpora and retrieval test collections through a separation of latent topical structure, surface...
Can LLM Rerankers Predict Their Own Ranking Performance?
arXiv:2606.03535v1 Announce Type: new Abstract: Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction (QPP) addresses this need, but most existing methods rely on external predictors after retrieval or reranking. In this paper, we study \textit{reranker-internal QPP}: can an LLM reranker estimate the quality of the ranking it has just produced?
Five Queries Are Enough: Query-Efficient and Surrogate-Free Membership Inference Attacks on RAG via Entailment
arXiv:2605.24312v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become central to large language model (LLM) deployments, grounding responses in enterprise or proprietary data to reduce hallucinations. However, this design introduces a new privacy risk: model outputs may signal the presence of specific documents in the retrieval corpus, enabling membership inference attacks (MIAs) that leak sensitive information. Existing MIAs are feasible, but they often rely on...