用词典构建低资源语言对话系统，效果优于通用模型。
From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages
- 将词典转化为指令-回复对，构建专用对话数据
- 120亿参数模型经4比特量化微调，性能达91.0分
- 适合有词典但无语料的低资源语言开发者
低资源语言在人工智能发展上面临核心挑战：缺乏大规模训练语料库难以构建专业对话系统。本文提出一种系统化方法，将结构化语言资源转化为专用AI系统，证明专家标注的词汇数据库可作为对话AI的有效基础。我们通过将印地语WordNet转换为125万条多样化的指令-回复对，利用4比特量化与高效LoRA技术微调120亿参数语言模型。通过印地语语言学习聊天机器人评估发现，基于结构化知识的系统在教学有效性上表现更优（91.0分），显著高于通用模型的79.4至83.6分，同时保持良好语义表现和极高一致性。该完整流程为具有WordNet资源的任何语言提供了专用AI开发的可行性范例。本研究填补了低资源语言AI可及性的关键空白，提供了一种替代语料密集型方法的实用路径，有望推动数百种具备现有WordNet资源的语言实现专业化AI开发。
原文摘要 · Abstract (English)
Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora. We present a systematic methodology for transforming structured linguistic resources into specialized AI systems, demonstrating that expert-curated lexical databases can serve as effective foundations for conversational AI development. Our approach converts Hindi WordNet into 1.25 million diverse instruction-response pairs, fine-tunes a 12B-parameter language model using resource-efficient LoRA with 4-bit quantization. Evaluation through a Hindi language learning chatbot demonstrates that structured-knowledge-based systems achieve superior pedagogical effectiveness (91.0 vs. 79.4-83.6 for general-purpose models) while maintaining competitive semantic performance and exceptional consistency. The complete pipeline demonstrates a proof-of-concept methodology using Hindi for developing specialized AI systems for any languages with WordNet resources. This work addresses the critical gap in AI accessibility for low-resource languages, offering a practical alternative to corpus-intensive approaches and potentially enabling specialized AI development for the hundreds of languages with existing WordNet resources.
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