Dpp-based Adversarial Prompt Searching For Lanugage Models
Zhang Xu, Wan Xiaojun. Arxiv 2024
[Paper]
Language models risk generating mindless and offensive content, which hinders their safe deployment. Therefore, it is crucial to discover and modify potential toxic outputs of pre-trained language models before deployment. In this work, we elicit toxic content by automatically searching for a prompt that directs pre-trained language models towards the generation of a specific target output. The problem is challenging due to the discrete nature of textual data and the considerable computational resources required for a single forward pass of the language model. To combat these challenges, we introduce Auto-regressive Selective Replacement Ascent (ASRA), a discrete optimization algorithm that selects prompts based on both quality and similarity with determinantal point process (DPP). Experimental results on six different pre-trained language models demonstrate the efficacy of ASRA for eliciting toxic content. Furthermore, our analysis reveals a strong correlation between the success rate of ASRA attacks and the perplexity of target outputs, while indicating limited association with the quantity of model parameters.
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