Summary of Protecting Your Llms with Information Bottleneck, by Zichuan Liu et al.
Protecting Your LLMs with Information Bottleneck
by Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 advent of large language models (LLMs) has brought significant advancements in natural language processing. However, their ability to generate harmful content remains a pressing concern. The authors introduce Information Bottleneck Protector (IBProtector), a defense mechanism grounded in the information bottleneck principle. IBProtector selectively compresses and perturbs prompts, preserving essential information for target LLMs to respond accurately. Empirical evaluations show that IBProtector outperforms current defenses against jailbreak attacks without compromising response quality or inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have transformed natural language processing, but they can also produce harmful content. To stop this from happening, researchers created a new way to protect these models called Information Bottleneck Protector (IBProtector). IBProtector makes sure the model only sees what it needs to give the right answer by squishing and messing with the prompt. It’s like putting a special filter on what the model can see. This helps keep the model safe from people trying to trick it. |
Keywords
» Artificial intelligence » Inference » Natural language processing » Prompt
