Dangerous speech detection is a well-studied task, but existing approaches typically treat utterances in isolation, relying on binary labels that ignore who is speaking and in what mental state. We formulate a context-dependent variant of this task by grounding it in Theory-of-Mind (ToM). In cognitive science, ToM studies how humans attribute latent mental states-such as emotions, intentions, and actions-to others. We argue that such states are key signals for assessing the risk of an utterance. Building on this view, we construct ToM-DS, a 79K-instance dataset where each utterance is paired with structured speaker profiles, ToM states (emotion, intent, action), and topic hierarchies. During data construction, we first identify context-dependent sentences and generate diverse safe and dangerous scenarios surrounding them. High-quality annotations are obtained with state-of-the-art LLMs and a multi-stage cross-agent validation pipeline, yielding a comprehensive and reliable resource for context-dependent dangerous speech detection and fine-grained risk level classification. We further propose ToMGuard, a lightweight model with a dynamic ToM attention mechanism that adaptively weighs different mental-state cues. ToMGuard outperforms strong proprietary and open-source LLMs with significantly fewer parameters. Experimental results show that ToMGuard sets a new benchmark for context-dependent dangerous speech detection and risk level classification on ToM-DS.

Emotional support conversation systems aim to help users alleviate distress through empathetic dialogue. However, existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. To address this, we propose a generalizable fine-grained problem enhancement method that systematically augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. As a demonstration, we construct EmoCare, a large-scale ESC dataset with 2.6K dialogues and 42.8K utterances, expanding problem type coverage from 13 to 45 fine-grained categories. Building on this data augmentation process, we introduce FPEMF, a flexible framework for empathetic dialogue generation, which comprises two modules: fine-grained problem enhancement and multi-dimensional feedback, which can be seamlessly integrated with various backbone models. The multi-dimensional feedback module evaluates responses from four perspectives: emotional understanding, strategy effectiveness, contextual consistency, and topic relevance, guiding models to generate more supportive replies. Experiments show that FPEMF consistently improves both automatic and human evaluation metrics across different models.