Papers by Ritwick Mishra
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values (2023.acl-srw)
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| Challenge: | Large language models (LMs) have been shown to be highly effective for identifying harmful training instances, but dataset size and model complexity constraints limit the ability to apply Shapley-based data valuation to fine-tuning large pre-trained language models. |
| Approach: | They propose an algorithm that aggregates Shapley values from subsets for valuation of entire training set and a value transfer method that leverages value information extracted from a simple classifier trained using representations from the target language model. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and can filter fine-tuning data to increase language model performance compared to training with the full fine-uning dataset. |