Summary of Alignment-enhanced Decoding:defending Via Token-level Adaptive Refining Of Probability Distributions, by Quan Liu et al.
Alignment-Enhanced Decoding:Defending via Token-Level Adaptive Refining of Probability Distributions
by Quan Liu, Zhenhong Zhou, Longzhu He, Yi Liu, Wei Zhang, Sen Su
First submitted to arxiv on: 14 Aug 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 proposed Alignment-Enhanced Decoding (AED) method aims to address jailbreak attacks on large language models by employing adaptive decoding and enhancing safety alignment while maintaining helpfulness. AED uses the Competitive Index to quantify alignment failures and computes post-alignment logits based on self-evaluation feedback, then adaptively combines these with original logits to obtain harmless and helpful distributions. The approach is evaluated across five models and four common jailbreaks, demonstrating its effectiveness. This paper provides a novel defense against jailbreak attacks by tackling the root cause of alignment failures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Jailbreak attacks on large language models can generate harmful content. To stop this, AED uses adaptive decoding to fix the problems that make it happen. It checks how well the model’s predictions align with what we want, and adjusts its decisions based on how good or bad they are. This makes sure the generated text is helpful and not harmful. The method was tested on five different models and four types of attacks, showing it works well. |
Keywords
* Artificial intelligence * Alignment * Logits
