Papers by Reshmi Ghosh
On Surgical Fine-tuning for Language Encoders (2023.findings-emnlp)
Copied to clipboard
Abhilasha Lodha, Gayatri Belapurkar, Saloni Chalkapurkar, Yuanming Tao, Reshmi Ghosh, Samyadeep Basu, Dmitrii Petrov, Soundararajan Srinivasan
| Challenge: | preserving knowledge of target distribution by fine-tuning all layers can be expensive and may increase data volume requirements. |
| Approach: | They propose an efficient metric based on the diagonal of the Fisher information matrix (FIM score) to select the candidate layers for selective fine-tuning. |
| Outcome: | The proposed metric can select layers leading to strong performance on GLUE and SuperGLUE tasks and across distinct language encoders. |
Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent studies have focused on poisoning during supervised fine-tuning, RLHF, or inference-time time optimization. |
| Approach: | They propose a simple fake reward attack that requires no access to the reward model and significantly increases vulnerability. |
| Outcome: | The proposed attack reduces the fake reward ASR from 0.23 to 0.07 without degrading utility. |