11 papers
The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise
MichaÅ Mazuryk, Fleur Dolmans, Louis Gehringer +3
Recent work has suggested that adding irrelevant documents to the input of retrieval-augmented generation (RAG) systems can improve question-answering performance, a phenomenon ref…
Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability
Jia-Huei Ju, Eugene Yang, Trevor Adriaanse +2
Long-form Retrieval-Augmented Generation (RAG) brings the challenge of coverage-based ranking, because ranking methods must ensure the inclusion of comprehensive relevant nuggets (…
Incorporating Q&A Nuggets into Retrieval-Augmented Generation
Laura Dietz, Bryan Li, Gabrielle Liu +5
RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System…
Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector
Thong Nguyen, Yibin Lei, Jia-Huei Ju +2
Learned Sparse Retrieval (LSR) combines the efficiency of bi-encoders with the transparency of lexical matching, but existing approaches struggle to scale beyond English. We introd…
LANCER: LLM Reranking for Nugget Coverage
Jia-Huei Ju, François G. Landry, Eugene Yang +2
Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG requires retrieval to provide documents covering a wide range of relevant…
To Case or Not to Case: An Empirical Study in Learned Sparse Retrieval
Emmanouil Georgios Lionis, Jia-Huei Ju, Angelos Nalmpantis +3
Learned Sparse Retrieval (LSR) methods construct sparse lexical representations of queries and documents that can be efficiently searched using inverted indexes. Existing LSR appro…