Papers by Prakash Bhat
Propulsion: Steering LLM with Tiny Fine-Tuning (2025.coling-main)
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| Challenge: | Propulsion is a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the model’s parameters. |
| Approach: | They propose a parameter-efficient fine-tuning method that selectively re-scales specific dimensions of a pre-trained model without modifying the parameters. |
| Outcome: | The proposed method reduces parameter count from 355.3 million to 0.086 million while maintaining competitive performance across benchmarks. |
Predicting Through Generation: Why Generation Is Better for Prediction (2025.acl-long)
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Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat, Chun-Nam Yu, Mojtaba Soltanalian, Ivan Garibay, Ozlem Garibay, Chen Chen, Niloofar Yousefi
| Challenge: | Large Language Models (LLMs) are increasingly used for predictive tasks such as classification and regression. |
| Approach: | They propose a framework that generates output tokens from mas-sive text corpora and a task adapter to ensure consistency between token generation and final prediction. |
| Outcome: | The proposed framework outperforms baseline models on classification and regression benchmarks and the proposed framework consistently outperformed standard baseline models. |