Papers by Swanand Ravindra Kadhe
SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging (2026.findings-acl)
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| Challenge: | Recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts. |
| Approach: | They propose a lightweight framework that restores safety while maintaining downstream performance. |
| Outcome: | The proposed framework reduces harmful outputs compared to other defenses, with negligible impact on utility. |
STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks often treat complex tasks as monolithic, resulting in inconsistent performance and inconsistent explanations. |
| Approach: | They propose a framework for creating controlled variations of benchmark tasks based on the concept of scaffolding, which introduces structured, incremental support in a step-by-step manner. |
| Outcome: | The proposed framework enables systematic probing of model behavior by identifying the specific reasoning skill compositions they lack. |
Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents (2025.findings-acl)
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Ivoline C. Ngong, Swanand Ravindra Kadhe, Hao Wang, Keerthiram Murugesan, Justin D. Weisz, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy
| Challenge: | Conversational agents are increasingly woven into individuals’ personal lives, yet users underestimate the privacy risks associated with them. |
| Approach: | They propose a framework that allows users to reformulate out-of-context information in user prompts by identifying and reformulating out- of-content information in the context. |
| Outcome: | The proposed framework can achieve strong gains in contextual privacy while preserving the user’s intended interaction goals. |