Papers by Arnav Chavan
A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models (2023.acl-long)
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| Challenge: | Existing literature demonstrates that compressing deep learning models could affect their fairness. |
| Approach: | They evaluate pruned, distilled, and quantized language models to assess their fairness . they also examine the impact of using multilingual models and evaluation measures . |
| Outcome: | The proposed methods can reduce the fairness of language models by reducing their complexity and reducing the cost of training and deployment. |
Surgical Feature-Space Decomposition of LLMs: Why, When and How? (2024.acl-long)
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| Challenge: | Low-rank approximations of the weight and feature space can enhance the performance of large language models. |
| Approach: | They propose to use weight and feature space decomposition to improve LLM performance . they also extend their investigation to the implications of low-rank approximations on model bias . |
| Outcome: | The proposed low-rank approximations can improve performance of large language models . the authors show that the approximate can improve generalization and inference performance . |