Papers by Mojtaba Soltanalian
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models use full finetunation, but this is impractical as language models continue to scale up. |
| Approach: | They propose a parameter-efficient fine-tuning method for large language models based on updating only a few rows and columns of the weight matrices in transformers. |
| Outcome: | The proposed method gives comparable or better accuracies than state-of-the-art methods while being more memory and computation-efficient. |
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. |