47 citations · 87 across the 9 of their papers we have counts for
9 papers
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni +2
The task of radiology reporting comprises describing and interpreting the medical findings in radiographic images, including description of their location and appearance. Automated…
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Iain Mackie, Ivan Sekulic, Shubham Chatterjee +2
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs…
Adaptive Latent Entity Expansion for Document Retrieval
Iain Mackie, Shubham Chatterjee, Sean MacAvaney +1
Despite considerable progress in neural relevance ranking techniques, search engines still struggle to process complex queries effectively - both in terms of precision and recall.…
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Iain Mackie, Shubham Chatterjee, Jeffrey Dalton
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance…
Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond
Paul Owoicho, Ivan Sekulić, Mohammad Aliannejadi +2
This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across…
Generative Relevance Feedback with Large Language Models
Iain Mackie, Shubham Chatterjee, Jeffrey Dalton
Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead o…
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data
Carlos Gemmell, Jeffrey Dalton
Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unli…
DocuT5: Seq2seq SQL Generation with Table Documentation
Elena Soare, Iain Mackie, Jeffrey Dalton
Current SQL generators based on pre-trained language models struggle to answer complex questions requiring domain context or understanding fine-grained table structure. Humans woul…
Query-Specific Knowledge Graphs for Complex Finance Topics
Iain Mackie, Jeffrey Dalton
Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses a…