3 papers
cs.CL2025
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
Jabez Magomere, Emanuele La Malfa, Manuel Tonneau +2
Online misinformation remains a critical challenge, and fact-checkers increasingly rely on claim matching systems that use sentence embedding models to retrieve relevant fact-check…
cs.IR2024
SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks
Michael Shliselberg, Ashkan Kazemi, Scott A. Hale +1
Diaspora communities are disproportionately impacted by off-the-radar misinformation and often neglected by mainstream fact-checking efforts, creating a critical need to scale-up e…
cs.CL2024
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat, Zhijing Jin, Artem Abzaliev +19
Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse t…