16 papers
Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage
Saron Samuel, Alexander Martin, Eugene Yang +5
Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relat…
Findings of the MAGMaR 2026 Shared Task
Alexander Martin, Dengjia Zhang, Joel Brogan +7
This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task partic…
ColBERTSaR: Sparsified ColBERT Index via Product Quantization
Eugene Yang, Andrew Yates, Dawn Lawrie +3
While ColBERT is an effective neural retrieval architecture, it requires a heavy index structure to support candidate set retrieval based on approximated token embeddings, gatherin…
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 (…
Overview of the TREC 2025 RAGTIME Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +3
The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has cr…
FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG
Maxime Dassen, Rebecca Kotula, Kenton Murray +5
Retrieval-Augmented Generation (RAG) models are critically undermined by citation hallucinations, a deceptive failure where a model cites a source that fails to support its claim.…