MENDER : Multi-hop Commonsense and Domain-specific C o T Reasoning for Knowledge-grounded Empathetic Counseling of Crime Victims (2025)

Abstract: Commonsense inference and domain-specific expertise are crucial for understanding and responding to emotional, cognitive, and topic-specific cues in counseling conversations with crime victims. However, these key evidences are often dispersed across multiple utterances, making it difficult to capture through single-hop reasoning. To address this, we propose MENDER, a novel Multi-hop commonsensE and domaiN-specific Chain-of-Thought (CoT) reasoning framework for knowleDge-grounded empathEtic Response generation in counseling dialogues. MENDER leverages large language models (LLMs) to integrate commonsense and domain knowledge via multi-hop reasoning over the dialogue context. It employs two specialized reasoning chains, viz. Commonsense Knowledge-driven CoT and Domain Knowledge-driven CoT rationales, which extract and aggregate dispersed emotional, cognitive, and topical evidences to generate knowledge-grounded empathetic counseling responses. Experimental evaluations on counseling dialogue dataset, POEM validate MENDER’s efficacy in generating coherent, empathetic, knowledge-grounded responses.

## introduction commonsense inference and domain expertise are crucial for effective mental health and legal counseling of crime victims @xcite . since victims often express trauma indirectly, counselors must infer unspoken emotions, intentions, and needs through commonsense reasoning @xcite to foster empathy and trust. in addition, victims need targeted mental health and legal support for their overall well-being. thus, counselors must have domain expertise to accurately diagnose and treat psychological conditions (brown, 2007), 1 code and sample dataset is available at https://github. com/abid839/mender/tree/main . ## related work recent efforts have focused on developing dialogue systems for mental health and legal counseling of crime victims @xcite @xcite @xcite , with an emphasis on using llms @xcite for automated counseling agents @xcite . integrating commonsense knowledge, such as emotional and cognitive insights, has been shown to improve empathy and contextual relevance @xcite @xcite , while domain-specific knowledge from external sources like wikipedia enhances factual accuracy and relevance @xcite @xcite . chain-of-thought (cot) prompting has been found to enhance reasoning by decomposing complex problems into manageable steps @xcite , and retrieval-augmented generation (rag) leverages external knowledge to improve accuracy and reliability @xcite . despite these advancements, many existing models still struggle to effectively integrate commonsense and domain knowledge, resulting in superficial responses. this work proposes a framework combining multi-hop commonsense reasoning with atomic knowledge @xcite and domain-specific reasoning using entity-guided retrieval-augmented generation (e-rag), to seamlessly generate coherent, empathetic, and knowledge-grounded responses. ## conclusion this work presents mender, a multi-hop reasoning framework that integrates commonsense and domain-specific knowledge for generating empathetic, knowledge-grounded responses in counseling dialogues. using commonsense and domain knowledge-driven cot rationales, mender captures emotional, cognitive, and topic-specific details to ensure coherent, empathetic, and informed responses. extensive experiments on the poem dataset demonstrate the promising potential of mender in generating coherent, empathetic, and knowledge-grounded responses, significantly improving the quality of counseling outcomes.