Papers by Kush Varshney
Biomedical Interpretable Entity Representations (2021.findings-acl)
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| Challenge: | Existing work on general interpretable representation learning does not transfer to biomedicine . pre-trained models induce dense entity representations but are not immediately interpretable. |
| Approach: | They propose a method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. |
| Outcome: | The proposed model performs well on biomedical tasks including disambiguation and label classification. |
Value Alignment from Unstructured Text (2024.emnlp-industry)
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Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush Varshney
| Challenge: | Currently, alignment of large language models to value systems relies on the availability of supervised and preference data. |
| Approach: | They propose a systematic approach for aligning large language models to values in unstructured text data using synthetic data generation techniques. |
| Outcome: | The proposed approach shows improved performance on the Mistral-7B-Instruct model compared to other approaches, as quantified through the use of automatic metrics and win rates. |