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    <abstract>“This paper presents our system for CCL25-Eval Task 10, addressing Fine-Grained Chinese Hate Speech Recognition (FGCHSR). We propose a novel SRAG-MAV framework that synergistically integrates task reformulation(TR), Self-Retrieval-Augmented Generation (SRAG), and Multi-Round Accumulative Voting (MAV). Our method reformulates the quadruplet extraction task into triplet extraction, uses dynamic retrieval from the training set to create contextual prompts,and applies multi-round inference with voting to improve output stability and performance. Our system, based on the Qwen2.5-7B model, achieves a Hard Score of 26.66, a Soft Score of 48.35,and an Average Score of 37.505 on the STATE ToxiCN dataset, significantly outperforming base-lines such as GPT-4o (Average Score 15.63) and fine-tuned Qwen2.5-7B (Average Score 35.365).The code is available at https://github.com/king-wang123/CCL25-SRAG-MAV.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition
%A Wang, Jiahao
%A Liu, Ramen
%A Zhang, Longhui
%A Li, Jing
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F wang-etal-2025-system-report
%X “This paper presents our system for CCL25-Eval Task 10, addressing Fine-Grained Chinese Hate Speech Recognition (FGCHSR). We propose a novel SRAG-MAV framework that synergistically integrates task reformulation(TR), Self-Retrieval-Augmented Generation (SRAG), and Multi-Round Accumulative Voting (MAV). Our method reformulates the quadruplet extraction task into triplet extraction, uses dynamic retrieval from the training set to create contextual prompts,and applies multi-round inference with voting to improve output stability and performance. Our system, based on the Qwen2.5-7B model, achieves a Hard Score of 26.66, a Soft Score of 48.35,and an Average Score of 37.505 on the STATE ToxiCN dataset, significantly outperforming base-lines such as GPT-4o (Average Score 15.63) and fine-tuned Qwen2.5-7B (Average Score 35.365).The code is available at https://github.com/king-wang123/CCL25-SRAG-MAV.”
%U https://aclanthology.org/2025.ccl-2.47/
%P 395-402

Markdown (Informal)

[System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition](https://aclanthology.org/2025.ccl-2.47/) (Wang et al., CCL 2025)

• System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition (Wang et al., CCL 2025)

ACL

• Jiahao Wang, Ramen Liu, Longhui Zhang, and Jing Li. 2025. System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 395–402, Jinan, China. Chinese Information Processing Society of China.