Adversarial Contrastive Decoding: Boosting Safety Alignment of Large Language Models via Opposite Prompt Optimization
Authors: Zhengyue Zhao, Xiaoyun Zhang, Kaidi Xu, Xing Hu, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen


Reviewer paper summaries: This paper proposes Adversarial Contrastive Decoding (ACD) to improve the safety capabilities of LLMs. This paper separately optimizes two opposing soft system prompts: a safeguarding prompt and an adversarial prompt. During the inference stage, the ptimized soft prompts are concatenated with  text embeddings for decoding.The  experiments on multiple benchmarks and various LLMs, demonstrate the effectiveness of ACD.
Summary of strengths: 1.This paper proposes a new method to automatically obtain contrastive prompts. Compared to previous methods ACD does not require manual design.
2.Using the generated contrastive soft prompts during inference can significantly enhance the security of the LLMS.
3.This paper conducts experiments on multiple LLMs of varying models and multiple multiple datasets to  demonstrate the effectiveness of ACD.
Summary of weaknesses: 1. Lack of detailed description of the training process. Both L_sp  and L_ap will  optimize the embedding layer of instructions, whether the two loss functions are optimized simultaneously, sequentially or in other ways?
2. There is a lack of analysis of the underlying causes for the results presented in Table 4.Why does the performance of null-ap drop significantly, while the performance degradation of sp-null is not so much.
Comments, suggestions and typos: The format of table names in the paper should be consistent, such as (Table 2 in line 420; Tab. 4 in line 463).
Overall assessment scores: 3.5