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20232026
most citedWhat Can Natural Language Processing Do for Peer Review?

5 citations · 40 across the 105 of their papers we have counts for

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Showing 2025 · cs.CLShow all

26 papers · 2 filters

cs.CL2025

M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset

Jiahui Geng, Jonathan Tonglet, Iryna Gurevych

Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover only one or two languages, focus on a single task, or rely on…

cs.CL2025

Citation Failure: Definition, Analysis and Efficient Mitigation

Jan Buchmann, Iryna Gurevych

Citations from LLM-based RAG systems are supposed to simplify response verification. However, this goal is undermined in cases of citation failure, where a model generates a helpfu…

cs.CL2025

The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors

Abdelrahman Sadallah, Tim Baumgärtner, Iryna Gurevych +1

Providing constructive feedback to paper authors is a core component of peer review. With reviewers increasingly having less time to perform reviews, automated support systems are…

cs.CL2025

Towards Automated Error Discovery: A Study in Conversational AI

Dominic Petrak, Thy Thy Tran, Iryna Gurevych

Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching u…

cs.CL2025

ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links

Serwar Basch, Ilia Kuznetsov, Tom Hope +1

Understanding fine-grained links between documents is crucial for many applications, yet progress is limited by the lack of efficient methods for data curation. To address this lim…

cs.CL2025

Is this chart lying to me? Automating the detection of misleading visualizations

Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars +1

Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccu…