Papers by Ramtin Pedarsani
Benchmarking and Mitigating the Impact of Noisy User Prompts in Medical VLMs via Cross-Modal Reflection (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing medical vision-language models follow user-provided prompts blindly, a new study finds . current models are noisy, causing problems with reliability in real-world interactions . |
| Approach: | They propose a method to evaluate the influence of clinical prompts on medical vision-language models . they use cross-modal reflection chain-of-thought to train the model to produce reasoning paths . |
| Outcome: | The proposed method significantly improves the robustness against noisy prompts . existing Med-VLMs follow user-provided prompts blindly, the authors show . |
Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing Med-VLMs are vulnerable to harmful clinical queries . authors propose a novel inference-time defense strategy to mitigate harmful queries based on synthetic clinical demonstrations . |
| Approach: | They propose a novel inference-time defense strategy to mitigate harmful queries . existing Med-VLMs are vulnerable to harmful queries, they argue . |
| Outcome: | The proposed strategy reduces query risk while reducing demonstration budget . existing Med-VLMs are vulnerable to harmful queries, authors argue . |
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures. |
| Approach: | They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity. |
| Outcome: | Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost. |