2 citations · 6 across the 8 of their papers we have counts for
9 papers
The Impact of Backbone Evolution on LLM-Based Relevance Assessments
Chuting Yu, Guido Zuccon, Teerapong Leelanupab
LLMs are evolving rapidly, with newer models offering stronger capabilities. This suggests that in LLM-based relevance judging, more capable models will achieve higher agreement wi…
ITER: Interaction-Aware Retrieval for Agentic Search
Haodong Chen, Shuai Wang, Yu Yin +3
Deep-research agents answer complex user questions through an iterative sequence of search steps, where the agent autonomously formulates sub-queries to retrieve the evidence neede…
More Efficient LLM Reranking with Whole-Pool, Setwise, Long-Context Language Models
Hang Li, Chuting Yu, Teerapong Leelanupab +2
LLM-based re-rankers produce rankings through repeated local comparisons (listwise, pairwise or pointwise), requiring many sequential model calls. We study how long-context LLMs ca…
Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking
Haodong Chen, Shengyao Zhuang, Zheng Yao +2
Zero-shot document re-ranking with Large Language Models (LLMs) has evolved from Pointwise methods to Listwise and Setwise approaches that optimize computational efficiency. Despit…
When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
Chuting Yu, Hang Li, Guido Zuccon +2
Human relevance assessment is time-consuming and cognitively intensive, limiting the scalability of Information Retrieval evaluation. This has led to growing interest in using larg…
Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering
Haodong Chen, Guido Zuccon, Teerapong Leelanupab
Genomic question answering often requires complex reasoning and integration across diverse biomedical sources. GeneGPT addressed this challenge by combining domain-specific APIs wi…
AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs
Xinyu Mao, Teerapong Leelanupab, Martin Potthast +2
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for in…
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
Xinyu Mao, Teerapong Leelanupab, Harrisen Scells +1
Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevan…
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang +2
In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised…