Chris Samarinas — accepted AI-conference papers

2025

Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation

Chris Samarinas, Alexander Krubner, Alireza Salemi, Youngwoo Kim, Hamed Zamani

ACL 2025finding

This paper presents ICAT, an evaluation framework for measuring coverage of diverse factual information in long-form text generation. ICAT breaks down a long output text into a list of atomic claims and not only verifies each claim through retrieval from a (reliable) knowledge source, but also compu…

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2021

Improving Evidence Retrieval for Automated Explainable Fact-Checking

Chris Samarinas, Wynne Hsu, Mong Li Lee

NAACL 2021system demonstrations

Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence r…

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