23 citations · 56 across the 14 of their papers we have counts for
13 papers · 1 filter
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…
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…
A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment
Negar Arabzadeh, Charles L. A . Clarke
Large Language Models (LLMs) are increasingly used to automate relevance judgments for information retrieval (IR) tasks, often demonstrating agreement with human labels that approa…
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models
Amin Bigdeli, Negar Arabzadeh, Ebrahim Bagheri +1
Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, w…
A Comparison of Methods for Evaluating Generative IR
Negar Arabzadeh, Charles L. A. Clarke
Information retrieval systems increasingly incorporate generative components. For example, in a retrieval augmented generation (RAG) system, a retrieval component might provide a s…
Generative Information Retrieval Evaluation
Marwah Alaofi, Negar Arabzadeh, Charles L. A. Clarke +1
In this chapter, we consider generative information retrieval evaluation from two distinct but interrelated perspectives. First, large language models (LLMs) themselves are rapidly…
Fréchet Distance for Offline Evaluation of Information Retrieval Systems with Sparse Labels
Negar Arabzadeh, Charles L. A. Clarke
The rapid advancement of natural language processing, information retrieval (IR), computer vision, and other technologies has presented significant challenges in evaluating the per…
Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers
Negar Arabzadeh, Amin Bigdeli, Charles L. A. Clarke
Large language models can now directly generate answers to many factual questions without referencing external sources. Unfortunately, relatively little attention has been paid to…