# Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques

arXiv: https://arxiv.org/abs/2408.07676 · HTML: https://mlsanity.com/paper/2408.07676/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations](https://mlsanity.com/paper/2405.16584/) (2024)

## Built on by (most cited first, 7 of 7)

- [Detecting Conversational Mental Manipulation with Intent-Aware Prompting](https://mlsanity.com/paper/2412.08414/) (2024)
- [Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication](https://mlsanity.com/paper/2502.08896/) (2025)
- [ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour](https://mlsanity.com/paper/2506.12090/) (2025)
- [CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues](https://mlsanity.com/paper/2506.04131/) (2025)
- [Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue](https://mlsanity.com/paper/2601.08342/) (2026)
- [Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language](https://mlsanity.com/paper/2505.18159/) (2025)
- [Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation](https://mlsanity.com/paper/2506.14211/) (2025)

## Abstract (arXiv)

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.