Enterprise AI Analysis
Prompt-Driven Large Language Model Merge for Fine-Grained Chinese Hate Speech Detection
The paper introduces a novel three-stage LLM-based framework (Prompt Engineering, Supervised Fine-tuning, LLM Merging) for fine-grained Chinese hate speech detection. It leverages the Qwen2.5-7B-Instruct LLM, achieving superior performance on the STATE-ToxiCN benchmark, demonstrating enhanced robustness against out-of-distribution cases and significant accuracy improvements over baselines. The framework addresses semantic complexity, incomplete information extraction, and generalization limitations inherent in traditional and directly applied LLMs for this challenging task.
Key Performance Indicators
Highlighting the core improvements and analytical outcomes from the research.
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Accuracy Improvement
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Hard Score (Ours)
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Soft Score (Ours)
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The proposed framework integrates prompt engineering, supervised fine-tuning, and LLM merging into a robust solution for hate speech detection.
Three-Stage Optimization Strategy
Prompt Engineering
→
Supervised Fine-tuning
→
LLM Merging
15%
Accuracy Improvement over baseline models
Domain-specific prompt templates are crucial for enhancing structured output and fine-grained hate judgment logic.
| Strategy | Score | Improvement (%) |
|---|---|---|
| ICL | 0.2921 | 0 |
| ICL+Non Hate | 0.3279 | 12.2 |
| ICL+NH+Category Explain | 0.3340 | 14.3 |
| ICL+NH+CE+Judge Criteria | 0.3436 | 17.6 |
|
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Dynamic LLM Merge effectively synthesizes diverse capabilities from fine-tuned models for enhanced generalization and robustness.
5.1%
Increase in Hard Score (0.2383 to 0.2504) indicates improved consensus on definitive cases.
Challenges & Future Directions in Merging
While merged models demonstrate robust performance, diminishing returns between Merge2 and Merge3 suggest potential limits to current merging strategies. This indicates a need for novel fusion techniques to address Chinese's context-dependent hate markers.
Key Takeaway: Hybrid approaches combining prompt engineering with model merging are essential for addressing Chinese hate speech's unique linguistic and cultural complexity.
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