"The proliferation of hate speech on Chinese social media poses urgent societal risks, yet traditional systems struggle to decode context-dependent rhetorical strategies and evolving slang. To bridge this gap, we propose a novel three-stage LLM-based framework: Prompt Engineering, Supervised Fine-tuning, and LLM Merging. First, context-aware prompts are designed to guide LLMs in extracting implicit hate patterns. Next, task-specific features are integrated during supervised fine-tuning to enhance domain adaptation. Finally, merging fine-tuned LLMs improves robustness against out-of-distribution cases. Evaluations on the STATE-ToxiCN benchmark validate the framework’s effectiveness, demonstrating superior performance over baseline methods in detecting fine-grained hate speech."

In the field of natural language generation (NLG), controlling text generation (CTG) is critical, particularly in query auto-completion (QAC) where the need for personalization and diversity is paramount. However, it is essentially challenging to adapt to various control objectives and constraints, which results in existing CTG approaches meeting with mixed success. This paper presents UCTG, a unified controllable text generation framework, which introduces a novel prompt learning method for CTG. Specifically, this framework seamlessly integrates a control module, a prompt module, and a generation module. The control module leverages a fine-tuned model to distill user preference features and behavioral patterns from historical data, incorporating human feedback into the model’s loss functions. These features are then transformed by the prompt module into vectors that guide the generation module. As such, the text generation can be flexibly controlled without modifying the task settings. By employing this unified approach, UCTG significantly improves query accuracy and coherence in tasks with different objectives and constraints, which is validated by extensive experiments on the Meituan and AOL real-world datasets. UCTG not only improves text generation control in QAC but also sets a new framework for flexible NLG applications.