The Art of Defending

The paper presents the Safety and Over-Defensiveness Evaluation (SODE) benchmark, which assesses Large Language Models’ (LLMs) defense strategies against safety concerns. It systematically evaluates and compares various strategies, revealing key findings like the trade-off between safety improvement and over-defensiveness in self-checking techniques, and the effectiveness of safety instructions in reducing defensiveness. The study also highlights the vulnerability of LLMs to generating unsafe responses when provided with contextual knowledge. Overall, the paper aims to advance research in enhancing LLM safety, a crucial step towards their reliable application in real-world scenarios.

For a detailed read, you can access the full paper here.