Summary of Tiny Refinements Elicit Resilience: Toward Efficient Prefix-model Against Llm Red-teaming, by Jiaxu Liu et al.
Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming
by Jiaxu Liu, Xiangyu Yin, Sihao Wu, Jianhong Wang, Meng Fang, Xinping Yi, Xiaowei Huang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to improving the safety and robustness of Large Language Models (LLMs) by developing a sentinel model that reconstructs input prompts with additional tokens. This plug-and-play prefix module can reduce toxicity in responses from target LLMs, addressing current deficiencies in red-teaming strategies for LLMs. The sentinel model overcomes parameter inefficiency and limited model accessibility issues for fine-tuning large target models. The approach employs Proximal Policy Optimization (PPO) with a value head-sharing mechanism to manage complex agent interactions. Extensive experiments across text-to-text and text-to-image tasks demonstrate the effectiveness of this framework in mitigating toxic outputs, even when using larger models like Llama-2, GPT-3.5, and Stable-Diffusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep language models safe from harmful responses by creating a new model that adds extra details to what you’re asking it. This makes the response less likely to be hurtful or offensive. The team used a special training method called Proximal Policy Optimization (PPO) to teach their model how to work well with other models. They tested their approach on different tasks and showed that it can make large language models like Llama-2, GPT-3.5, and Stable-Diffusion less likely to produce toxic responses. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Gpt » Llama » Optimization
