activity
20242026
most citedDemographically-Inspired Query Variants Using an LLM

5 citations · 12 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Memory Makes the Difference: Evaluating How Different Memory Roles Shape Conversational Agents

Yuxin Wang, Paul Thomas, Zhiwei Yu +5

Prior research on memory mechanism in RAG-based conversational system has emphasized how memory is stored and retrieved. However, far less is known about how memories with differen…

cs.CL2025

All Claims Are Equal, but Some Claims Are More Equal Than Others: Importance-Sensitive Factuality Evaluation of LLM Generations

Miriam Wanner, Leif Azzopardi, Paul Thomas +3

Existing methods for evaluating the factuality of large language model (LLM) responses treat all claims as equally important. This results in misleading evaluations when vital info…

cs.IR2025★ 5 cited

Demographically-Inspired Query Variants Using an LLM

Marwah Alaofi, Nicola Ferro, Paul Thomas +2

This study proposes a method to diversify queries in existing test collections to reflect some of the diversity of search engine users, aligning with an earlier vision of an 'ideal…

cs.IR2025★ 2 cited

Judging the Judges: A Collection of LLM-Generated Relevance Judgements

Hossein A. Rahmani, Clemencia Siro, Mohammad Aliannejadi +6

Using Large Language Models (LLMs) for relevance assessments offers promising opportunities to improve Information Retrieval (IR), Natural Language Processing (NLP), and related fi…

cs.IR2024

LLMJudge: LLMs for Relevance Judgments

Hossein A. Rahmani, Emine Yilmaz, Nick Craswell +6

The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation…

cs.IR2024★ 2 cited

Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024

Hossein A. Rahmani, Clemencia Siro, Mohammad Aliannejadi +6

The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference…

cs.IR2024★ 3 cited

SynDL: A Large-Scale Synthetic Test Collection for Passage Retrieval

Hossein A. Rahmani, Xi Wang, Emine Yilmaz +3

Large-scale test collections play a crucial role in Information Retrieval (IR) research. However, according to the Cranfield paradigm and the research into publicly available datas…