Fine-Grained Chinese Hate Speech Detection: A Prompt-Driven LLM Merge Approach
Analysis
This research explores merging large language models (LLMs) to enhance fine-grained hate speech detection in Chinese, a crucial area for mitigating online toxicity. The work's reliance on prompt engineering for the merged LLMs warrants further investigation into its robustness and generalizability across diverse data distributions.
Key Takeaways
- •Investigates the use of LLM merging for hate speech detection.
- •Focuses specifically on the Chinese language and fine-grained detection.
- •Employs a prompt-driven approach for the merged models.
Reference / Citation
View Original"The study focuses on prompt-driven LLM merge for fine-grained Chinese hate speech detection."