Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. |
| Approach: | They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training. |
| Outcome: | The proposed task improves distribution case-to-code induction and various coding generation tasks. |
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| Challenge: | Recent development of large language models (LLMs) for code shows promise in achieving code intelligence. |
| Approach: | They explore the ability of large language models to generate automated test cases . they show +11.77% and +4.22% higher code pass rates on HumanEval+ . |
| Outcome: | The proposed models show higher pass rates on humanEval+ compared with the current state-of-the-art models. |
UnitCoder: Scalable Code Synthesis from Pre-training Corpora (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at code understanding and generation, yet code generation remains a challenge. |
| Approach: | They propose a model that supervises pre-training data quality through automatically generated unit tests while ensuring correctness via an iterative fix and refine flow. |
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Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models (2025.acl-industry)
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Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran, Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Large Language Models (LLMs) require high quality instruction data for effective alignment, especially in code generation tasks where expert curated datasets are expensive to produce. |
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Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
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| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
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Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)
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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che
| Challenge: | Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity. |
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SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task (2025.coling-main)
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| Challenge: | Existing efforts to bolster LLMs’ proficiency in Cypher generation are hindered by the lack of annotated datasets of Query-Cypher pairs. |
| Approach: | They propose a method for constructing a synthetic Query-Cypher pair dataset using LLM prompting and template-filling. |
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Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (2023.emnlp-main)
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| Challenge: | Recent studies have explored using large language models to generate synthetic datasets . however, the effectiveness of the LLM-generated synthetic data is inconsistent across different classification tasks. |
| Approach: | They propose to use large language models to generate synthetic datasets to better understand factors that moderate the effectiveness of LLM-generated synthetic data. |
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Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have great potential for synthetic data generation. |
| Approach: | They show that large language models can generate useful data even for complex tasks . they use a symmetric task difficulty asymmetry to prompt an LLM to generate plausible input text for a target output structure. |
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Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)
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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li
| Challenge: | Large language models (LLMs) have shown impressive performance in code understanding and generation. |
| Approach: | They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks. |
| Outcome: | The proposed models have shown to be highly effective in coding tasks. |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |