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Francis Ferraro

ACL 2026×3推理可解释性评测与基准多模态检索与知识
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0 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #3/3ACL 2026Poster· Natural Language Processing

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

Seyedali Mohammadi · Manas Gaur · Francis Ferraro

摘要

Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or refute it. We frame feasibility assessment as a diagnostic reasoning task in which, given a hypothesis, a model predicts feasible or infeasible and ju...

可解释性推理评测与基准OpenReview 项目页
作者位次 #7/8ACL 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
作者位次 #3/3ACL 2026Findings· Findings of ACL

DRP : Distilled Reasoning Pruning with Mathematical Skill-aware Step Decomposition for Efficient Large Reasoning Models

Yuxuan Jiang · Dawei Li · Francis Ferraro

摘要

While Large Reasoning Models (LRMs) excel at complex tasks via long Chain-of-Thought (CoT) reasoning, their outputs are often excessively verbose, leading to inefficiency. This problem is amplified when the student’s long-form reasoning mismatches the concise outputs of smaller teacher models—com...

推理效率与系统对齐与偏好OpenReview 项目页

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