VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation (2025.findings-emnlp)
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| Challenge: | Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction. |
| Approach: | They propose a large-scale instruction tuning dataset for Python-based visualization and self-correction. |
| Outcome: | The proposed dataset outperforms strong open-source baselines and proprietary models like GPT-4o-mini. |
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
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Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback (2024.emnlp-main)
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| Challenge: | Recent work shows that Code Large Language Models can address a wide range of code-related tasks. |
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FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)
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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu
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| Challenge: | Existing approaches to improve UI code generation rely on expensive human feedback or distilling a proprietary model. |
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How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data (2024.emnlp-main)
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Yejie Wang, Keqing He, Dayuan Fu, Zhuoma GongQue, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, Weiran Xu
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Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)
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| Challenge: | Code large language models (LLMs) enhance programming by understanding and generating code across languages. |
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DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows (2024.acl-long)
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| Challenge: | Large language models (LLMs) have become a dominant tool for NLP researchers in a wide range of tasks. |
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Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs (2025.naacl-srw)
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| Challenge: | Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. |
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CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
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