# 无资源、无基准、没问题？评估与改进LLM在无资源语言上的代码生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-15 08:00
- AIHOT 分数：45
- AIHOT 链接：https://aihot.virxact.com/items/cmqkrekvo006ysl4z49jz0t27
- 原文链接：https://arxiv.org/abs/2606.16827

## AI 摘要

针对LLM几乎未见训练数据的无资源编程语言，团队构建并发布了三个代码生成基准。实验发现，在目标语言上进一步预训练能最大提升性能，但直接用于指令微调模型会损害指令遵循能力。为此，从基础模型出发，先预训练再通过权重差异迁移从指令模型注入指令遵循能力，显著提升了无资源场景的代码生成表现，使公司能以低成本部署专用指令模型。

## 正文

Large Language Models (LLMs) have significantly advanced the automation of software engineering tasks. One prominent example is code generation, where an LLM produces code in a specified programming language based on a natural language description. Most research in this area has focused on high-resource languages, such as Python or Java, which benefit from abundant training data. A smaller body of work has explored low-resource languages, which are underrepresented in training corpora. In contrast, no-resource languages for which LLMs have seen virtually no training data remain largely unstudied. These languages often emerge in industry, where organizations develop proprietary or domain-specific languages unsupported by commercial tools like GitHub Copilot. This results in the need for companies to deploy their own in-house code recommenders. To investigate possible solutions in this context, we build and release three code generation benchmarks for no-resource languages, based on two recently proposed programming languages for which very little training data is available. Using these benchmarks, we experiment several solutions to teach LLMs about no-resource languages, including prompt-based techniques as well as pre-training and fine-tuning exploiting the little data available. While further pre-training gives the largest performance gains for no-resource languages, applying it directly to instruction-tuned models harms their ability to follow instructions. To address this, we start from a base model, further pre-training it on the target language, and then inject instruction-following capabilities via weight diff transfer from an instruction model. Such an approach significantly improves code generation capabilities in no-resource settings, allowing companies to cheaply deploy a specialized instruct model without dealing with the computational cost of instruction fine-tuning.