3 citations · 12 across the 13 of their papers we have counts for
14 papers
Beyond Output Critique: Self-Correction via Task Distillation
Hossein A. Rahmani, Mengting Wan, Pei Zhou +4
Large language models (LLMs) have shown promising self-correction abilities, where iterative refinement improves the quality of generated responses. However, most existing approach…
Self-Correcting Large Language Models: Generation vs. Multiple Choice
Hossein A. Rahmani, Satyapriya Krishna, Xi Wang +2
Large language models have recently demonstrated remarkable abilities to self-correct their responses through iterative refinement, often referred to as self-consistency or self-re…
Overview of the TREC 2023 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +5
This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels a…
Towards Understanding Bias in Synthetic Data for Evaluation
Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz +2
Test collections are crucial for evaluating Information Retrieval (IR) systems. Creating a diverse set of user queries for these collections can be challenging, and obtaining relev…
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation
Ruizhe Li, Chen Chen, Yuchen Hu +3
Retrieval-Augmented Generation (RAG) leverages large language models (LLMs) combined with external contexts to enhance the accuracy and reliability of generated responses. However,…
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…
JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment
Hossein A. Rahmani, Emine Yilmaz, Nick Craswell +1
The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors -- a process…
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…
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…
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…
Adaptive Retrieval-Augmented Generation for Conversational Systems
Xi Wang, Procheta Sen, Ruizhe Li +1
Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting exte…
Synthetic Test Collections for Retrieval Evaluation
Hossein A. Rahmani, Nick Craswell, Emine Yilmaz +2
Test collections play a vital role in evaluation of information retrieval (IR) systems. Obtaining a diverse set of user queries for test collection construction can be challenging,…
Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
Hossein A. Rahmani, Xi Wang, Mohammad Aliannejadi +2
Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance. Poorly formulated quest…
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Xi Wang, Hossein A. Rahmani, Jiqun Liu +1
Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However…