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Code Generation for Niche Languages: Challenges and Solutions

Automated code generation has made significant progress through large language models (LLMs). These models learn the syntax, semantics, and usage patterns of programming languages from extensive and diverse datasets. However, for niche languages, characterized by a small amount of training data, this data scarcity presents a challenge. The ability of LLMs to generalize effectively is limited, leading to poorer performance in code generation compared to widely used languages. Therefore, there is a need for techniques that can close this performance gap.

The Search for the Optimal Strategy

A recent study investigates the effectiveness of various approaches to improve LLM performance for niche languages. The examined methods include:

Classic Fine-tuning: This method is limited by the small amount of data. Variants of In-Context Learning: This involves using prompts to provide the LLM with additional information about the niche language, e.g., through a few examples that illustrate the features of the target language. Pre-training that teaches the model the translation between widespread and niche languages. The study focuses on two niche languages (R and Racket) and six LLMs with different architectures and sizes.

Results of the Study

The results show that fine-tuning is generally the best choice for smaller LLMs. This is possibly because even a small dataset is sufficient to train their limited number of parameters. As the size of the models increases, in-context learning becomes increasingly effective and represents a safe and cost-effective option. While it always improves performance, it does so to varying degrees. In contrast, very large LLMs can worsen their performance on niche languages through fine-tuning, possibly due to the lack of data required to effectively update their weights.

In-Context Learning as a Promising Approach

Further investigations suggest that in-context learning, particularly for larger models with over 30 billion parameters, represents a reliable and cost-effective method. While fine-tuning offers advantages for smaller models, in-context learning seems to be the more robust strategy for larger LLMs.

Conclusion: No Silver Bullet, but Promising Approaches

The study clarifies that there is no universal "silver bullet" to improve code generation for niche languages. The optimal strategy depends on the size of the LLM and the available resources. While fine-tuning can be effective for smaller models, in-context learning proves to be a more promising approach for larger LLMs. However, research in this area is not yet complete, and further investigations are needed to exploit the full potential of LLMs for code generation in niche languages. Especially the development of new techniques for the effective use of limited datasets will be crucial to close the performance gap between widespread and niche languages.

Bibliography: Giagnorio, A., Martin-Lopez, A., & Bavota, G. (2025). Enhancing Code Generation for Low-Resource Languages: No Silver Bullet. arXiv preprint arXiv:2501.19085. Brooks, F. P. (1987). No Silver Bullet: Essence and Accidents of Software Engineering. Computer, 20(4), 10-19. https://arxiv.org/abs/2501.19085
https://arxiv.org/html/2501.19085v1
https://github.com/codefuse-ai/Awesome-Code-LLM
https://www.researchgate.net/publication/385606870_Not_the_Silver_Bullet_LLM-enhanced_Programming_Error_Messages_are_Ineffective_in_Practice
https://www.researchgate.net/publication/220477127_No_Silver_Bullet_Essence_and_Accidents_of_Software_Engineering
https://edocs.tib.eu/files/e01fn17/882143263.pdf
https://openreview.net/forum?id=y0GJXRungR
https://aclanthology.org/2024.emnlp-main.1199/
https://www.sciencedirect.com/science/article/pii/S0268401223000233
https://en.wikipedia.org/wiki/Assembly_language ```