Papers by Ian Foster

2 papers
A Browser-based Open Source Assistant for Multimodal Content Verification (2026.eacl-demo)

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Challenge: Disinformation and advanced generative AI content pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media.
Approach: They propose to integrate a browser-based tool that automatically extracts content from a suite of backend NLP classifiers and presents actionable credibility signals and AI-generation likelihood in an easy-to-digest format.
Outcome: The Verification Assistant is a browser-based tool that extracts content and routes it to a suite of backend NLP classifiers, presenting actionable credibility signals, AI-generation likelihood, and other verification advice in an easy-to-digest format.
The Diminishing Returns of Masked Language Models to Science (2023.findings-acl)

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Challenge: Existing studies have shown that masked language models can improve downstream tasks by pretraining larger models for longer on more data.
Approach: They empirically evaluate the extent to which these results extend to tasks in science by using 14 domain-specific transformer-based masked language models.
Outcome: The proposed model can improve on 12 scientific tasks, but not all.

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