DPP-Based Adversarial Prompt Searching for Lanugage Models
Xu Zhang Xiaojun Wan
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.