Papers by Shubham Mohole
A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling (2025.findings-emnlp)
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Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan, Weicheng Ma, Hefan Zhang, Sophia Pan, Ming Cheng, Shubham Mohole, Saeed Hassanpour, Soroush Vosoughi, Michael Macy
| Challenge: | Rhetorical strategies are important to persuasive communication, but their analysis relies on human annotation, which is costly, inconsistent and difficult to scale. |
| Approach: | They propose a framework that leverages large language models to generate and label debate data . they fine-tune transformer-based classifiers on this dataset and validate it against human data a . |
| Outcome: | The proposed model achieves high performance and strong generalization across topical domains. |
Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication (2025.naacl-long)
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Weicheng Ma, Hefan Zhang, Ivory Yang, Shiyu Ji, Joice Chen, Farnoosh Hashemi, Shubham Mohole, Ethan Gearey, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. |
| Approach: | They propose a multi-LLM communication framework that facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. |
| Outcome: | The proposed framework excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. |
VeriMinder: Mitigating Analytical Vulnerabilities in NL2SQL (2025.acl-demo)
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| Challenge: | Application systems using natural language interfaces to databases (NLIDBs) have democratized data analysis, but they are not without significant risks. |
| Approach: | They propose an interactive system that detects and mitigates cognitive biases in analytical questions by using contextual semantic mapping frameworks. |
| Outcome: | The proposed system detects and mitigates cognitive biases in analytical questions and generates high-quality, task-specific prompts. |