Papers by Peter Zeng
Re-Examining FactBank: Predicting the Author’s Presentation of Factuality (2022.coling-1)
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| Challenge: | Previously published results on FactBank are no longer valid. |
| Approach: | They propose to correct a subset of FactBank data to improve performance . they use multiple training paradigms, data smoothing techniques, and polarity classifiers . |
| Outcome: | The proposed model improves performance on the FactBank dataset. |
Residualized Similarity for Faithfully Explainable Authorship Verification (2025.findings-emnlp)
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Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey, Nikita Soni, Niranjan Balasubramanian, Owen Rambow, H. Schwartz
| Challenge: | Neural methods achieve high accuracy, but their representations lack direct interpretability. |
| Approach: | They propose a method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability. |
| Outcome: | The proposed method improves the performance of state-of-the-art models while maintaining interpretability. |
LVLMs and Humans Ground Differently in Referential Communication (2026.acl-long)
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Peter Zeng, Weiling Li, Amie J. Paige, Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras, Gregory J. Zelinsky, Susan Brennan, Owen Rambow
| Challenge: | generative AI agents cannot model common ground in a way that enables smooth communication . a recent study examined whether large language models and large vision language models engage in grounding as human discourse partners do . |
| Approach: | They propose to use referential communication to model common ground between a pair of directors and a picture matching system. |
| Outcome: | The proposed experiment shows that generative AI agents cannot model common ground . human conversation relies on common ground accrued and updated by interacting partners . |
Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground (2024.findings-acl)
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Adil Soubki, John Murzaku, Arash Yousefi Jordehi, Peter Zeng, Magdalena Markowska, Seyed Abolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing benchmarks for theory of mind (ToM) use synthetic data, which can misalign with human behavior. |
| Approach: | They propose a question-answer benchmark based on naturally occurring spoken dialogs to evaluate theory of mind capabilities of language models. |
| Outcome: | The proposed dataset shows that LMs struggle to demonstrate theory of mind (ToM) . |
Synthetic Audio Helps for Cognitive State Tasks (2025.findings-naacl)
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| Challenge: | Prior work in NLP focuses on tasks that involve extracting information about the cognitive states of human entities from text. |
| Approach: | They propose a framework for learning to add synthetic audio to text-only corpora and a system that automatically tracks audio signals to produce naturalistic audio. |
| Outcome: | The proposed framework improves on 7 cognitive state modeling tasks on text and synthetic audio data from an off-the-shelf TTS system. |