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William Gantt Walden

ACL 2026×2SIGIR 2026检索与知识多模态推理数据与预训练评测与基准
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2 一作 · 3 篇 · ACL 2026 / SIGIR 2026

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2026 顶会论文

3
作者位次 #3/8ACL 2026Findings· Findings of ACL

W iki V ideo: Article Generation from Multiple Videos

Alexander Martin · Reno Kriz · William Gantt Walden · Kate Sanders · Hannah Recknor · Eugene Yang · Francis Ferraro · Benjamin Van Durme

摘要

We introduce the task of grounded article generation with the goal of creating a Wikipedia-style article from multiple diverse videos about real-world events—from natural disasters to political elections—where all the information in the article is supported by video evidence. Videos are intuitive...

多模态检索与知识推理OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

How Grounded is W ikipedia? A Study on Structured Evidential Support and Retrieval

William Gantt Walden · Kathryn Ricci · Miriam Wanner · Zhengping Jiang · Chandler May · Rongkun Zhou · Benjamin Van Durme

摘要

Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipedia—its groundedness in its cited sources—is vital to this purpose. This work analyzes both how grounded Wikipedia is...

检索与知识推理数据与预训练OpenReview 项目页
一作SIGIR 2026Poster· Information Retrieval (Demo)

Auto-ARGUE: LLM-Based Report Generation Evaluation

William Gantt Walden · Marc Mason · Orion Weller · Laura Dietz · John M. Conroy · Neil Molino · Hannah Recknor · Bryan Li · Gabrielle Kaili-May Liu · Yu Hou · Dawn Lawrie · James Mayfield · Eugene Yang

摘要

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 designed for report generation are lacking. Accordingly, we introduce Auto-ARGUE, a robust LLM-based implementation o...

检索与知识多模态评测与基准OpenReview

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