9 papers
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…
A Reproducibility Study of LLM-Based Query Reformulation
Amin Bigdeli, Radin Hamidi Rad, Hai Son Le +4
Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substantial effectiveness gains. Howeve…
ReFormeR: Learning and Applying Explicit Query Reformulation Patterns
Amin Bigdeli, Mert Incesu, Negar Arabzadeh +2
We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits…
QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation
Amin Bigdeli, Radin Hamidi Rad, Mert Incesu +3
We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent…
Adversarial Attacks against Neural Ranking Models via In-Context Learning
Amin Bigdeli, Negar Arabzadeh, Ebrahim Bagheri +1
While neural ranking models (NRMs) have shown high effectiveness, they remain susceptible to adversarial manipulation. In this work, we introduce Few-Shot Adversarial Prompting (FS…
Benchmarking LLM-based Relevance Judgment Methods
Negar Arabzadeh, Charles L. A. Clarke
Large Language Models (LLMs) are increasingly deployed in both academic and industry settings to automate the evaluation of information seeking systems, particularly by generating…