做特殊教育NLP或低资源语言模型微调的团队，这篇论文提供了一个可复现的CGFD流程，直接解决了繁体中文IEP生成的数据稀缺和隐私问题，值得点开看具体实现。
该研究提出了一种基于语料库特征扩散（CGFD）的低资源微调流程，用于自动生成繁体中文个别化教育计划（IEP）。通过25个专家标注的高分种子转录本提取特征，并注入LLM提示以驱动扩散，最终获得567个有效扩散样本，结合15个专家金种子构建582样本训练集，微调Breeze-7B模型。实验发现，在繁体中文token预算下，语法约束解码（GCD）反而降低效率，无GCD路径在55样本压力测试中实现100%模式通过率且延迟降低34%。在10样本正式测试中，无GCD路径的BERTScore F1达0.779，超越GPT-5.4等零样本基线，且完全本地化、气隙推理。该系统填补了繁体中文特殊教育NLP的空白，提供了隐私保护的工业级解决方案。
Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusion
Writing Individualized Education Programs (IEPs) is a high-labor, knowledge-intensive document burden; English-language research has demonstrated that generative AI can significantly reduce drafting time, yet automated IEP generation in Traditional Chinese remains virtually unexplored due to domain data scarcity, strict privacy regulations, and the absence of local evaluation benchmarks. We propose a low-resource fine-tuning pipeline centered on Corpus-Grounded Feature Diffusion (CGFD): (1) 25 dual-expert high-score seed transcripts are selected via a tau threshold with flag-aware score caps; (2) a FeatureProfile (sentence length, structure, quantification templates) is extracted from seeds and injected into LLM prompts alongside Verbalized-Sampling-style diversity control to drive diffusion; (3) 15 expert gold seeds are used as diffusion anchors, targeting 585 samples; 567 valid diffusion samples are obtained, yielding a 582-sample training set used to fine-tune Breeze-7B with QLoRA; (4) schema-constrained inference via Grammar-Constrained Decoding (GCD) enforces a hierarchical SMART Goal Ladder schema at inference time. Ablation results on a 55-sample schema stress set reveal an unexpected finding: GCD is counterproductive under Traditional Chinese token budgets -- the no-GCD path achieves 100% schema pass rate at 34% lower median latency, outperforming GCD on both reliability and speed. On the n=10 formal hold-out, the no-GCD inference path achieves BERTScore F1 = 0.779, exceeding GPT-5.4 (0.726), DeepSeek-V3.2 (0.703), Gemini-3-Flash-Preview (0.703), and Llama-4-Maverick (0.700) zero-shot baselines while maintaining fully local, air-gapped inference. This system addresses a gap in Traditional Chinese special-education NLP and offers a scalable, privacy-preserving local inference solution under an industrial engineering paradigm.