Data-efficient LLM Fine-tuning for Code Generation

• URL: http://arxiv.org/abs/2504.12687v1
• Date: Thu, 17 Apr 2025 06:29:28 GMT
• Title: Data-efficient LLM Fine-tuning for Code Generation
• Authors: Weijie Lv, Xuan Xia, Sheng-Jun Huang,
• Abstract summary: Large language models (LLMs) have demonstrated significant potential in code generation tasks.<n>Existing approaches typically generate large amounts of synthetic data for fine-tuning.<n>We propose a data selection strategy in order to improve the effectiveness and efficiency of training for code-based LLMs.
• Score: 30.441431693349866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated significant potential in code generation tasks. However, there remains a performance gap between open-source and closed-source models. To address this gap, existing approaches typically generate large amounts of synthetic data for fine-tuning, which often leads to inefficient training. In this work, we propose a data selection strategy in order to improve the effectiveness and efficiency of training for code-based LLMs. By prioritizing data complexity and ensuring that the sampled subset aligns with the distribution of the original dataset, our sampling strategy effectively selects high-quality data. Additionally, we optimize the tokenization process through a "dynamic pack" technique, which minimizes padding tokens and reduces computational resource consumption. Experimental results show that when training on 40% of the OSS-Instruct dataset, the DeepSeek-Coder-Base-6.7B model achieves an average performance of 66.9%, surpassing the 66.1% performance with the full dataset. Moreover, training time is reduced from 47 minutes to 34 minutes, and the peak GPU memory decreases from 61.47 GB to 42.72 GB during a single epoch. Similar improvements are observed with the CodeLlama-Python-7B model on the Evol-Instruct dataset. By optimizing both data selection and tokenization, our approach not only improves model performance but also improves training efficiency.

Related papers

• Efficient Alignment of Large Language Models via Data Sampling [0.4915744683251149]
  We propose an information theory-based methodology for efficient alignment by identifying a small high quality subset.<n>We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset.
  arXiv  Detail & Related papers  (2024-11-15T19:36:15Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs [30.441431693349866]
  Existing methods generate vast amounts of synthetic data for fine-tuning, leading to inefficiencies in training. CodeACT introduces the Complexity and Diversity Aware Sampling (CDAS) method to select high-quality training data. CodeACT achieves an 8.6% performance increase on HumanEval, reduces training time by 78%, and decreases peak GPU memory usage by 27%.
  arXiv  Detail & Related papers  (2024-08-05T02:38:48Z)
• Crafting Efficient Fine-Tuning Strategies for Large Language Models [2.633490094119608]
  Fine-tuning large language models (LLMs) with as few as 200 samples can improve model accuracy from 70% to 88% in a product attribute extraction task. A bayesian hyperparameter optimization method, which evaluates models at 20% of total training time, correlates strongly with final model performance. This approach led to a 2% improvement in accuracy over baseline models when evaluated on an independent test set.
  arXiv  Detail & Related papers  (2024-07-18T21:36:00Z)
• Entropy Law: The Story Behind Data Compression and LLM Performance [115.70395740286422]
  We find that model performance is negatively correlated to the compression ratio of training data, which usually yields a lower training loss. Based on the findings of the entropy law, we propose a quite efficient and universal data selection method. We also present an interesting application of entropy law that can detect potential performance risks at the beginning of model training.
  arXiv  Detail & Related papers  (2024-07-09T08:14:29Z)
• Text Quality-Based Pruning for Efficient Training of Language Models [66.66259229732121]
  We propose a novel method for numerically evaluating text quality in large unlabelled NLP datasets. By proposing the text quality metric, the paper establishes a framework to identify and eliminate low-quality text instances. Experimental results over multiple models and datasets demonstrate the efficacy of this approach.
  arXiv  Detail & Related papers  (2024-04-26T18:01:25Z)
• From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search [30.88999109835329]
  In text-based person search endeavors, data generation has emerged as a prevailing practice, addressing concerns over privacy preservation and the arduous task of manual annotation. We observe that only a subset of the data in constructed datasets plays a decisive role. We introduce a new Filtering-WoRA paradigm, which contains a filtering algorithm to identify this crucial data subset and WoRA learning strategy for light fine-tuning.
  arXiv  Detail & Related papers  (2024-04-16T05:29:14Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Efficient Online Data Mixing For Language Model Pre-Training [101.45242332613944]
  Existing data selection methods suffer from slow and computationally expensive processes. Data mixing, on the other hand, reduces the complexity of data selection by grouping data points together. We develop an efficient algorithm for Online Data Mixing (ODM) that combines elements from both data selection and data mixing.
  arXiv  Detail & Related papers  (2023-12-05T00:42:35Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.