Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques

Ivory Yang, Xiaobo Guo, Sean Xie, Soroush Vosoughi

Keywords: Mental Manipulation Detection, Chain-of-Thought, LLM, Prompting

TL;DR: For complex models to perform well in mental manipulation detection, Chain-of-Thought prompting needs to be paired with example-based learning.

Abstract: This study presents a comprehensive, long-term project to explore the effectiveness of various prompting techniques in detecting dialogical mental manipulation. We implement Chain-of-Thought prompting with Zero-Shot and Few-Shot settings on a binary mental manipulation detection task, building upon existing work conducted with Zero-Shot and Few-Shot prompting. Our primary objective is to decipher why certain prompting techniques display superior performance, so as to craft a novel framework tailored for detection of mental manipulation. Preliminary findings suggest that advanced prompting techniques may not be suitable for more complex models, if they are not trained through example-based learning.

Submission Number: 31