5 papers · 1 filter
Seek: Self-Evaluative Exploration for Knowledge Retrieval
Amin Bigdeli, Radin Hamidi Rad, Negar Arabzadeh +4
LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set, leaving re…
Route Me If You Can: A Benchmark for Query Reformulation Selection
Hai Son Le, Negar Arabzadeh, Amin Bigdeli +4
LLM-based query reformulation can improve retrieval, but no single reformulation strategy is consistently optimal across queries, domains, retrievers, or model backbones. This crea…
ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback
Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad +3
LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-document…
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem
Sajad Ebrahimi, Sara Salamat, Negar Arabzadeh +2
The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual…