SAP

홈페이지 · 논문 13편

The SAP benchmark is a significant development in the realm of attack prompt generation for red teaming and defending large language models (LLMs). Let's delve into the details: 1. Objective: - The primary goal of the SAP benchmark is to evaluate the safety and robustness of LLMs against red teaming attacks. - Red teaming attacks involve inducing LLMs to generate harmful or inappropriate content. 2. Methodology: - The SAP benchmark combines both manual and automatic methods to generate high-quality attack prompts. - It leverages the impressive capabilities of newly emerged LLMs. - Specifically, it instructs LLMs to mimic human-generated prompts through in-context learning. - The attack framework is designed to create these prompts. 3. Defense Framework: - In addition to attacking LLMs, the SAP benchmark proposes a defense framework. - This framework fine-tunes victim LLMs through iterative interactions with the attack framework. - The goal is to enhance the safety of LLMs against red teaming attacks. 4. Validation and Datasets: - Extensive experiments on different LLMs validate the effectiveness of both the attack and defense frameworks. - As part of this work, the authors release a series of attack prompt datasets named SAP with varying sizes. - These datasets facilitate safety evaluation and enhancement for a broader range of LLMs¹.