Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909067452088320 |
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| author | Gao, Shuzheng Mao, Wenxin Gao, Cuiyun Li, Li Hu, Xing Xia, Xin Lyu, Michael R. |
| author_facet | Gao, Shuzheng Mao, Wenxin Gao, Cuiyun Li, Li Hu, Xing Xia, Xin Lyu, Michael R. |
| contents | Pre-trained code models have recently achieved substantial improvements in many code intelligence tasks. These models are first pre-trained on large-scale unlabeled datasets in a task-agnostic manner using self-supervised learning, and then fine-tuned on labeled datasets in downstream tasks. However, the labeled datasets are usually limited in size (i.e., human intensive efforts), which may hinder the performance of pre-trained code models in specific tasks. To mitigate this, one possible solution is to leverage the large-scale unlabeled data in the tuning stage by pseudo-labeling. However, directly employing the pseudo-labeled data can bring a large amount of noise, i.e., incorrect labels, leading to suboptimal performance. How to effectively leverage the noisy pseudo-labeled data is a challenging yet under-explored problem.In this paper, we propose a novel approach named HINT to improve pre-trained code models with large-scale unlabeled datasets by better utilizing the pseudo-labeled data. HINT includes two main modules: HybrId pseudo-labeled data selection and Noise-tolerant Training. In the hybrid pseudo-data selection module, considering the robustness issue, apart from directly measuring the quality of pseudo labels through training loss, we further propose to employ a retrieval-based method to filter low-quality pseudo-labeled data. The noise-tolerant training module aims to further mitigate the influence of errors in pseudo labels by training the model with a noise-tolerant loss function and by regularizing the consistency of model predictions.The experimental results show that HINT can better leverage those unlabeled data in a task-specific way and provide complementary benefits for pre-trained models, e.g., improving the best baseline model by 15.33%, 16.50%, and 8.98% on code summarization, defect detection, and assertion generation, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01060 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models Gao, Shuzheng Mao, Wenxin Gao, Cuiyun Li, Li Hu, Xing Xia, Xin Lyu, Michael R. Software Engineering Pre-trained code models have recently achieved substantial improvements in many code intelligence tasks. These models are first pre-trained on large-scale unlabeled datasets in a task-agnostic manner using self-supervised learning, and then fine-tuned on labeled datasets in downstream tasks. However, the labeled datasets are usually limited in size (i.e., human intensive efforts), which may hinder the performance of pre-trained code models in specific tasks. To mitigate this, one possible solution is to leverage the large-scale unlabeled data in the tuning stage by pseudo-labeling. However, directly employing the pseudo-labeled data can bring a large amount of noise, i.e., incorrect labels, leading to suboptimal performance. How to effectively leverage the noisy pseudo-labeled data is a challenging yet under-explored problem.In this paper, we propose a novel approach named HINT to improve pre-trained code models with large-scale unlabeled datasets by better utilizing the pseudo-labeled data. HINT includes two main modules: HybrId pseudo-labeled data selection and Noise-tolerant Training. In the hybrid pseudo-data selection module, considering the robustness issue, apart from directly measuring the quality of pseudo labels through training loss, we further propose to employ a retrieval-based method to filter low-quality pseudo-labeled data. The noise-tolerant training module aims to further mitigate the influence of errors in pseudo labels by training the model with a noise-tolerant loss function and by regularizing the consistency of model predictions.The experimental results show that HINT can better leverage those unlabeled data in a task-specific way and provide complementary benefits for pre-trained models, e.g., improving the best baseline model by 15.33%, 16.50%, and 8.98% on code summarization, defect detection, and assertion generation, respectively. |
| title | Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2401.01060 |