Papers by Anubrata Das
On Localizing and Deleting Toxic Memories in Large Language Models (2025.findings-naacl)
Copied to clipboard
Anubrata Das, Manoj Kumar, Ninareh Mehrabi, Anil Ramakrishna, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Morteza Ziyadi, Rahul Gupta
| Challenge: | Existing methods to reduce toxic generation in large language models are not fully understood. |
| Approach: | They propose to understand the mechanisms that drive toxic generation in large language models by using memory localization to reduce toxic generation. |
| Outcome: | The proposed method reduces toxic generation from 62.86% to 28.61%, but it also improves generation quality. |
ProtoTEx: Explaining Model Decisions with Prototype Tensors (2022.acl-long)
Copied to clipboard
| Challenge: | Neural models for NLP have yielded significant gains in predictive accuracy across tasks. |
| Approach: | They propose a white-box NLP classification architecture based on prototype networks . they propose an interleaved training algorithm that faithfully explains model decisions . |
| Outcome: | The proposed model matches BART-large and exceeds BERTlarge on propaganda detection tasks. |