Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 14 Aug 2024.
Builds on
4 of 12 references.
References · 4
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Large Language Model Guided Tree-of-Thoughtcited by 218
- 2024MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversationscited by 21
Built on by
Citing papers per year: 2024: 1; 2025: 5; 2026: 1.
7 papers.
- 2024Detecting Conversational Mental Manipulation with Intent-Aware Promptingcited by 16
- 2025Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communicationcited by 5
- 2025ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviourcited by 4
- 2025CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialoguescited by 1
- 2026Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialoguecited by 1
- 2025Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Languagecited by 0
- 2025Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentationcited by 0
- 2026Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue13 Jan 2026
- 2025Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation17 Jun 2025
- 2025ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour11 Jun 2025
- 2025CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues4 Jun 2025
- 2025Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language10 May 2025
- 2025Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication13 Feb 2025
- 2024Detecting Conversational Mental Manipulation with Intent-Aware Prompting11 Dec 2024
Similar, not cited
Close in wording, no citation either way
- 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
- 2025The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance
- 2024Large Language Models are Null-Shot Learners
- 2023Prompt Your Mind: Refine Personalized Text Prompts within Your Mind
- 2025Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation
- 2026EchoGuard: An Agentic Framework with Knowledge-Graph Memory for Detecting Manipulative Communication in Longitudinal Dialogue
- 2025Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
- 2024Towards Generalist Prompting for Large Language Models by Mental Models