3 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
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,…
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…
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…