Papers by Shaz Furniturewala
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis (2024.findings-acl)
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| Challenge: | Recent studies have shown that user-level features can carry more task-related information than the text itself. |
| Approach: | They evaluate multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users’ self-expression and psychological attributes. |
| Outcome: | The proposed models improve on the language and traits of users, while lacking rich annotations of other attributes. |
“Thinking” Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models (2024.emnlp-main)
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Shaz Furniturewala, Surgan Jandial, Abhinav Java, Pragyan Banerjee, Simra Shahid, Sumit Bhatia, Kokil Jaidka
| Challenge: | Existing debiasing techniques are typically training-based or require access to the model’s internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs. |
| Approach: | They propose a system-based iterative framework that uses System 2 thinking processes to induce logical, reflective, and critical text generation with single, multi-step, instruction, and role-based variants. |
| Outcome: | The proposed framework significantly improves over other frameworks demonstrating lower mean bias in the outputs with competitive performance on the downstream tasks. |
Learning Through Dialogue: Engagement and Efficacy Matter More Than Explanations (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users’ learning and engagement are understudied. |
| Approach: | They analyze linguistic and interactional features from LLM and participant chats to identify the mechanisms and conditions under which LLM explanations shape changes in political knowledge and confidence. |
| Outcome: | The results show that LLM explanations shape political knowledge and confidence . they also show that their effects are highly conditional and vary by political efficacy . |