6 papers
Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Agentic Retrieval-Augmented Generation (RAG) has become a promising paradigm for multi-hop question answering, where a reasoning model iteratively issues queries to a retriever and…
RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Query Performance Prediction (QPP) estimates the retrieval quality of ranking models without the use of any human-assessed relevance judgements, and finds applications in query-spe…
Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
The quality of answers generated by large language models (LLMs) in retrieval-augmented generation (RAG) is largely influenced by the contextual information contained in the retrie…
Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Leveraging query variants (QVs), i.e., queries with potentially similar information needs to the target query, has been shown to improve the effectiveness of query performance pred…
Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG
Fangzheng Tian, Jinyuan Fang, Debasis Ganguly +2
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm,…
Is Relevance Propagated from Retriever to Generator in RAG?
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Retrieval Augmented Generation (RAG) is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection, as a part of a prom…