62 citations · 226 across the 45 of their papers we have counts for
28 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…
RAG over Thinking Traces Can Improve Reasoning Tasks
Negar Arabzadeh, Wenjie Ma, Sewon Min +1
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as ma…
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…
Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
Negar Arabzadeh, Andrew Drozdov, Michael Bendersky +1
Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling the generation of multiple se…
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…
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…
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…
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…
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…
Ranked List Truncation for Large Language Model-based Re-Ranking
Chuan Meng, Negar Arabzadeh, Arian Askari +2
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candida…
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…
Query Performance Prediction using Relevance Judgments Generated by Large Language Models
Chuan Meng, Negar Arabzadeh, Arian Askari +2
Query performance prediction (QPP) aims to estimate the retrieval quality of a search system for a query without human relevance judgments. Previous QPP methods typically return a…
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…
Retrieving Supporting Evidence for Generative Question Answering
Siqing Huo, Negar Arabzadeh, Charles L. A. Clarke
Current large language models (LLMs) can exhibit near-human levels of performance on many natural language-based tasks, including open-domain question answering. Unfortunately, at…
Retrieving Supporting Evidence for LLMs Generated Answers
Siqing Huo, Negar Arabzadeh, Charles L. A. Clarke
Current large language models (LLMs) can exhibit near-human levels of performance on many natural language tasks, including open-domain question answering. Unfortunately, they also…
Query Performance Prediction: From Ad-hoc to Conversational Search
Chuan Meng, Negar Arabzadeh, Mohammad Aliannejadi +1
Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgme…
Early Stage Sparse Retrieval with Entity Linking
Dahlia Shehata, Negar Arabzadeh, Charles L. A. Clarke
Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations…