Papers by Saeed Hassanpour
Addressing Healthcare-related Racial and LGBTQ+ Biases in Pretrained Language Models (2024.findings-naacl)
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| Challenge: | Pretrained language models (PLMs) propagate social stigmas and stereotypes, a critical concern given their widespread use. |
| Approach: | They adapt two intrinsic bias benchmarks to quantify racial and LGBTQ+ biases in prevalent PLMs and empirically evaluate the effectiveness of various debiasing methods in mitigating these biase. |
| Outcome: | The proposed methods reduce biases without compromising performance in downstream tasks. |
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for interpreting LLMs are post hoc and focus on low-level features and lack of explainability at higher-level text units. |
| Approach: | They propose a prototypical network-based white-box framework that allows LLMs to learn immediately interpretable embeddings during the fine-tuning stage while maintaining competitive performance. |
| Outcome: | The proposed framework can learn interpretable embeddings during the fine-tuning stage while maintaining competitive performance. |
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. |
Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)
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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness. |
| Approach: | They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. |
| Outcome: | The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
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| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations (2024.acl-long)
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| Challenge: | Existing studies on mental manipulation focus on context-free content and face challenges in identifying implicit toxicity. |
| Approach: | They propose a dataset that analyzes mental manipulation and its components . they propose to use 4,000 fictional dialogues to identify the techniques utilized for manipulation . |
| Outcome: | The proposed dataset enables a comprehensive analysis of mental manipulation . it shows that leading-edge models inadequately identify and categorize manipulative content . |
Improving Syntactic Probing Correctness and Robustness with Control Tasks (2023.acl-short)
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Weicheng Ma, Brian Wang, Hefan Zhang, Lili Wang, Rolando Coto-Solano, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Syntactic probing methods are biased by the PLMs’ memorization of common word co-occurrences, even if they do not form syntactical relations. |
| Approach: | They propose to use random word substitution and random label matching to reduce these biases and improve the robustness of syntactic probing methods. |
| Outcome: | The proposed tasks improve probing results and consistency between probing methods and make them more generalizable to unseen text 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. |