科尼语LLM：为低资源印度语言的多脚本指令微调与评估
Konkani LLM: Multi-Script Instruction Tuning and Evaluation for a Low-Resource Indian Language
AI总结：
本文提出Konkani-Instruct-100k数据集，通过Gemini 3生成，开发了优化区域特色的科尼语LLM，并构建多脚本评估基准，提升低资源语言在机器翻译中的表现。
摘要：
大型语言模型（LLMs）在低资源语言上下文中如科尼语表现不佳，这源于训练数据稀缺和高脚本多样性。为解决此问题，我们引入Konkani-Instruct-100k数据集，通过Gemini 3生成。我们通过评估Llama 3.1、Qwen2.5和Gemma 3等开源模型及专有闭源模型建立严格基准。主要贡献是开发了优化区域特色的科尼语LLM，并正在开发多脚本科尼语基准以促进跨脚本语言评估。在机器翻译中，科尼语LLM在相应基础模型上表现一致，并在某些设置中超越专有基线。
英文摘要：
Large Language Models (LLMs) consistently under perform in low-resource linguistic contexts such as Konkani. This performance deficit stems from acute training data scarcity compounded by high script diversity across Devanagari, Romi and Kannada orthographies. To address this gap, we introduce Konkani-Instruct-100k, a comprehensive synthetic instruction-tuning dataset generated through Gemini 3. We establish rigorous baseline benchmarks by evaluating leading open-weights architectures including Llama 3.1, Qwen2.5 and Gemma 3 alongside proprietary closed-source models. Our primary contribution involves the development of Konkani LLM, a series of fine-tuned models optimized for regional nuances. Furthermore, we are developing the Multi-Script Konkani Benchmark to facilitate cross-script linguistic evaluation. In machine translation, Konkani LLM delivers consistent gains over the corresponding base models and is competitive with and in several settings surpasses proprietary baselines
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
