In-Context Probing for Membership Inference in Fine-Tuned Language Models
Zhexi Lu · Hongliang Chi · Nathalie Baracaldo · Swanand Ravindra Kadhe · Yuseok Jeon · Lei Yu
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Zhexi Lu · Hongliang Chi · Nathalie Baracaldo · Swanand Ravindra Kadhe · Yuseok Jeon · Lei Yu
Aladin Djuhera · Swanand Ravindra Kadhe · Farhan Ahmed · Syed Zawad · Holger Boche
Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts. Many methods to realign safety have been proposed, bu...
Sungeun An · Swanand Ravindra Kadhe · Shailja Thakur · Chad DeLuca · Hima Patel
Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight into compositional skill gaps of LLMs and how to improve them. To make these weaknesses visible, we propose Scaffolded Task Design (STaD) framew...