collaborators

5 papers

cs.IR2026

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…

cs.CL2026

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation

Bryan Li, William Walden, Yu Hou +6

Evaluation of long-form, citation-backed reports has lately received significant attention due to the wide-scale adoption of retrieval-augmented generation (RAG) systems. Core to m…

cs.IR2026

Auto-ARGUE: LLM-Based Report Generation Evaluation

William Walden, Marc Mason, Orion Weller +10

Generation of citation-backed reports is a primary use case for retrieval-augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, tools…

cs.IR2026

Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

Laura Dietz, Bryan Li, Eugene Yang +3

RAG systems are increasingly evaluated and optimized using LLM judges, an approach that is rapidly becoming the dominant paradigm for system assessment. Nugget-based approaches in…

cs.IR2026

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…