Analyzing Jailbreaking in LLMs Through Pragmatics and Bias Studies

This work focuses on Large Language Models (LLMs) and some issues they pose. In particular, we delve into the phenomenon of jailbreaking, which refers to the ability to bypass LLMs’ restrictions and elicit problematic content through specific prompts. Jailbreaking is examined from various perspectives: first, we review existing literature categorizing jailbreaking prompts; second, we compare the literature on jailbreaking with the literature on bias in Natural Language Processing (NLP); third, we provide a pragmatic analysis of jailbreaking based on human deception strategies.