Challenge: Existing approaches to detect mental manipulations are limited due to complexity of detecting subtle, covert tactics in conversations.
Approach: They propose an approach to detect mental manipulations using large language models using intent-aware prompting by capturing the intents of participants.
Outcome: The proposed approach significantly reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases.

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MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations (2024.acl-long)

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Challenge: Existing studies on mental manipulation focus on context-free content and face challenges in identifying implicit toxicity.
Approach: They propose a dataset that analyzes mental manipulation and its components . they propose to use 4,000 fictional dialogues to identify the techniques utilized for manipulation .
Outcome: The proposed dataset enables a comprehensive analysis of mental manipulation . it shows that leading-edge models inadequately identify and categorize manipulative content .
SELF-PERCEPT: Introspection Improves Large Language Models’ Detection of Multi-Person Mental Manipulation in Conversations (2025.acl-short)

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Challenge: Mental manipulation is subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims.
Approach: They propose a dataset of 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions drawn from reality shows that mimic real-life scenarios.
Outcome: The proposed framework shows that it can detect multi-person, multi-turn mental manipulation in multi-people conversations.
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting (2023.findings-emnlp)

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Challenge: Existing systems for mental health support are shallow and heuristic, e.g., analyzing emotions and generating comforting responses.
Approach: They propose to use cognitive distortion detection to perform diagnosis on the patient’s speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts and schema analysis to summarize the cognition schemas.
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Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search (2023.findings-emnlp)

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Challenge: Existing methods for understanding users’ contextual search intent show unsatisfactory effectiveness and robustness to handle real conversational search scenarios.
Approach: They propose to use large language models to generate multiple query rewrites and hypothetical responses and to aggregate them into an integrated representation that can robustly represent the user’s real contextual search intent.
Outcome: The proposed framework can generate multiple query rewrites and hypothetical responses and can be used to represent the user’s real contextual search intent.
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation (2025.findings-emnlp)

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Challenge: Prior work has shown that intent detection enhances LLMs’ moderation guardrails, but the robustness of these guardrail mechanisms under malicious manipulations remains under-explored.
Approach: They propose a two-stage intent-based prompt-refinement framework that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives.
Outcome: The proposed framework outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses.
Deciphering Cognitive Distortions in Patient-Doctor Mental Health Conversations: A Multimodal LLM-Based Detection and Reasoning Framework (2024.emnlp-main)

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Challenge: Cognitive distortion research sheds light on pervasive errors in thinking patterns . authors present method for detecting and reasoning about cognitive distortions .
Approach: They propose a method for detecting and reasoning about cognitive distortions using Large Language Models.
Outcome: The proposed method improves accuracy and depth of detection and reasoning tasks in a zero-shot manner.
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)

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Challenge: Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level.
Approach: They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers.
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Metacognitive Prompting Improves Understanding in Large Language Models (2024.naacl-long)

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Challenge: Recent advances in prompting have enhanced reasoning in logic-intensive tasks for LLMs, yet the nuanced understanding abilities of these models remain underexplored.
Approach: They propose a strategy inspired by human introspective reasoning processes to enhance LLMs' understanding abilities.
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Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition (2023.emnlp-main)

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Challenge: Large Language Models are increasingly being deployed in interactive contexts that involve direct user engagement.
Approach: They run a global prompt hacking competition to encourage research on prompt hacks . they elicit 600K+ adversarial prompts against three state-of-the-art LLMs based on a dataset .
Outcome: The results of the competition show that current LLMs can be manipulated via prompt hacking . the competition elicits 600K+ adversarial prompts against three state-of-the-art LLM models .
SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals (2025.findings-emnlp)

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Challenge: Existing safety mechanisms for large language models (LLMs) are inadequate to fully leverage their internal cognitive processes.
Approach: They propose a framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions.
Outcome: The proposed framework reduces harmful outputs by approximately 80% while maintaining strong utility.

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