Papers by Gourab Dey
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks (2024.eacl-short)
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Gourab Dey, Adithya V Ganesan, Yash Kumar Lal, Manal Shah, Shreyashee Sinha, Matthew Matero, Salvatore Giorgi, Vivek Kulkarni, H. Schwartz
| Challenge: | Social science NLP tasks require large data to capture semantics and implicit pragmatics. |
| Approach: | They propose an open-source instruction tuning tool for social science NLP tasks that captures implicit pragmatic cues from text. |
| Outcome: | The proposed model matches or improves on a state-of-the-art, multi-task finetuned model on 80% of social tasks. |
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. |
SWAN: Semantic Watermarking with Abstract Meaning Representation (2026.acl-long)
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Ziping Ye, Gourab Dey, Christos Christodoulopoulos, Charith Peris, Anil Ramakrishna, Weitong Ruan, Aram Galstyan, Kai-Wei Chang, Rahul Gupta, Ninareh Mehrabi
| Challenge: | Existing methods to embed signatures by adjusting token selection preferences during text generation are highly sensitive to paraphrasing and synonyms. |
| Approach: | They propose a framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). |
| Outcome: | Empirical evaluation shows SWAN matches state-of-the-art detection performance on unaltered watermarked text while improving robustness against paraphrasing. |