Papers by Subhrangshu Nandi
Prompt Perturbation Consistency Learning for Robust Language Models (2024.findings-eacl)
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Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan
| Challenge: | Large language models have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. |
| Approach: | They propose a method to reduce the performance drop of large language models by regularizing the divergence between losses from clean and perturbed samples. |
| Outcome: | The proposed approach recovers on average 59% and 69% of the performance drop for IC and SF tasks while using ten times fewer augmented data samples. |
Measuring and Mitigating Local Instability in Deep Neural Networks (2023.findings-acl)
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| Challenge: | Uncertain details like random initialization can change the outputs of a trained system with potentially disastrous consequences. |
| Approach: | They propose a model stability problem by studying how the predictions of a deep neural network change as a consequence of stochasticity in the training process. |
| Outcome: | The proposed method outperforms data-agnostic methods and is 90% cheaper than the gold standard. |