Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation’s nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept .

One of the first steps in the judicial processis finding the applicable statutes/laws basedon the facts of the current situation. Manu-ally searching through multiple legislation andlaws to find the relevant statutes can be time-consuming, making the Legal Statute Identi-fication (LSI) task important for reducing theworkload, helping improve the efficiency ofthe judicial system. To address this gap, wepresent a novel knowledge graph-enhanced ap-proach for Legal Statute Identification (LSI) inIndian legal documents using Large LanguageModels, incorporating structural relationshipsfrom the Indian Penal Code (IPC) the main leg-islation codifying criminal laws in India. Onthe IL-TUR benchmark, explicit KG inferencesignificantly enhances recall without sacrific-ing competitive precision. Augmenting LLMprompts with KG context, though, merely en-hances coverage at the expense of precision,underscoring the importance of good rerank-ing techniques. This research provides the firstcomplete IPC knowledge graph and shows thatorganized legal relations richly augment statuteretrieval, subject to being integrated into lan-guage models in a judicious way. Our code anddata are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)