activity
20242026
collaborators

10 papers

cs.CL2026

On the Role of Citations in Preference Data

Yu Hou, Hal Daumé, Rachel Rudinger +1

Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against model hallucination and as a me…

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.CL2026

Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation

Alexander Martin, William Walden, Reno Kriz +5

We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a prevalent source of information online…

cs.SE2026

Can Coding Agents Reproduce Findings in Computational Materials Science?

Ziyang Huang, Yi Cao, Ali K. Shargh +15

Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is uncle…

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…