CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards
arXiv:2606.00020v1 Announce Type: new Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction. We propose CSRP, a three-stage framework that progressively builds correction capability through Continual Pre-training (CPT) on 5.9M balanced samples to internalize domain knowledge, Ch
The continuous advancements in Large Language Models necessitate specialized solutions for non-English languages to overcome limitations of general-purpose models and optimize for practical, precision-focused applications.
This development indicates a growing sophistication in AI model training, specifically for non-English languages, addressing critical challenges like over-correction and lack of linguistic nuance in specialized tasks.
The focus shifts from general LLMs to specialized, efficient, and linguistically precise models for tasks like Chinese Text Correction, moving beyond mere grammatical accuracy to contextual and cultural nuance.
- · AI researchers in non-English NLP
- · Chinese tech companies
- · Language service providers
- · LLM developers focusing on specialized tasks
- · General-purpose LLMs for specialized non-English tasks
- · Companies relying solely on broad SFT approaches
Improved accuracy and efficiency in Chinese grammatical error correction tools.
Increased demand for domain-specific pre-training and reinforcement learning techniques across various non-English NLP applications.
Enhanced capabilities for AI systems to understand and generate non-English content with greater fluency and cultural appropriateness, potentially impacting cross-cultural communication and content creation.
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Read at arXiv cs.CL