System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition
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Abstract (arXiv)
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 baselines 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 24 Jul 2025.
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16 of 22 references.
References · 16
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- 2020Detecting Hate Speech in Multi-modal Memescited by 38
4 more
- 2025Parallel Scaling Law for Language Modelscited by 29
- 2023A Two-Stage Adaptation of Large Language Models for Text Rankingcited by 17
- 2021A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Fillingcited by 16
- 2025STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detectioncited by 9
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Citing papers per year: 2026: 2.
2 papers.
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