20 citations · 38 across the 14 of their papers we have counts for
14 papers
The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam +1
Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings. In this…
uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
Simon Lupart, Kidist Amde Mekonnen, Zahra Abbasiantaeb +1
This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems across four domains (financ…
Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents
Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb +3
Deep research agents have emerged as LLM-based systems designed to perform multi-step information seeking and reasoning over large, open-domain sources to answer complex questions…
Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search
Clemencia Siro, Zahra Abbasiantaeb, Yifei Yuan +2
Conversational search systems increasingly employ clarifying questions to refine user queries and improve the search experience. Previous studies have demonstrated the usefulness o…
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi +1
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing rob…
Query Understanding in LLM-based Conversational Information Seeking
Yifei Yuan, Zahra Abbasiantaeb, Yang Deng +1
Query understanding in Conversational Information Seeking (CIS) involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities…
Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets
Zahra Abbasiantaeb, Simon Lupart, Leif Azzopardi +2
The rise of personalized conversational search systems has been driven by advancements in Large Language Models (LLMs), enabling these systems to retrieve and generate answers for…
Improving the Reusability of Conversational Search Test Collections
Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi +1
Incomplete relevance judgments limit the reusability of test collections. When new systems are compared to previous systems that contributed to the pool, they often face a disadvan…
IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search
Simon Lupart, Zahra Abbasiantaeb, Mohammad Aliannejadi
The Interactive Knowledge Assistant Track (iKAT) 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge…
Can We Use Large Language Models to Fill Relevance Judgment Holes?
Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi +1
Incomplete relevance judgments limit the re-usability of test collections. When new systems are compared against previous systems used to build the pool of judged documents, they o…
TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee +2
Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and respondi…
Generating Multi-Aspect Queries for Conversational Search
Zahra Abbasiantaeb, Simon Lupart, Mohammad Aliannejadi
Conversational information seeking (CIS) systems aim to model the user's information need within the conversational context and retrieve the relevant information. One major approac…
TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview
Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee +2
Conversational Information Seeking has evolved rapidly in the last few years with the development of Large Language Models providing the basis for interpreting and responding in a…
Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions
Zahra Abbasiantaeb, Yifei Yuan, Evangelos Kanoulas +1
Conversational question-answering (CQA) systems aim to create interactive search systems that effectively retrieve information by interacting with users. To replicate human-to-huma…