mlsanityData to 7 Oct 2026

Safurai-Csharp: Harnessing Synthetic Data to improve language-specific Code LLM

arXiv 2311.03243 ↗First posted 6 Nov 2023cs.CLcited by 0

On the map: Synthetic data and self-improvement for LLMs → Code and instruction data synthesis

Abstract (arXiv)

This paper introduces Safurai-Csharp, an open-source model designed to specialize in the generation, completion, and debugging of C# code. Safurai-Csharp is built upon the novel CodeLlama 34B model and leverages the EvolInstruct technique, creating a refined and expanded dataset for its fine-tuning process. The results of its performance, a notable score of 56.33% on the Manual MultiPL-E benchmark (Zero-Shot, Pass@1), signal its high capacity to streamline developers' workflows and aid code learning. It shows promise in setting new stakes in the landscape of open-source C# LLMs and hopes to inspire more inclusive and wide-ranging development in the field of language-specific LLMs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Code and instruction data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 19 of its 19 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

19 of 19 references.

References · 19

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  6. 2023Mistral 7Bcited by 3,065
  7. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  8. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  9. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  10. 2023StarCoder: may the source be with you!cited by 790
  11. 2023Textbooks Are All You Needcited by 561
  12. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 559
7 more
  1. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  2. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 294
  3. 2023SantaCoder: don't reach for the stars!cited by 169
  4. 2023PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedbackcited by 69
  5. 2022When Language Model Meets Private Librarycited by 54
  6. 2022Training and Evaluating a Jupyter Notebook Data Science Assistantcited by 31
  7. 2022ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languagescited by 29

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  4. 2025Holistic Evaluation of State-of-the-Art LLMs for Code Generation
  5. 2025Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
  6. 2024Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository
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