6 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
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,…
Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
Craig Macdonald, Jinyuan Fang, Andrew Parry +1
Search engines often follow a pipeline architecture, where complex but effective reranking components are used to refine the results of an initial retrieval. Retrieval augmented ge…
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multi-hop question answering (QA). However, their retrieval process faces two key challenges:…
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…