CodeFort: Robust Training for Code Generation Models

• URL: http://arxiv.org/abs/2405.01567v1
• Date: Thu, 11 Apr 2024 17:04:22 GMT
• Title: CodeFort: Robust Training for Code Generation Models
• Authors: Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras,
• Abstract summary: Code generation models are not robust to small perturbations. CodeFort is a framework to improve the robustness of code generation models. We show that we improve the average robust pass rates of baseline CodeGen models from 14.79 to 21.74.
• Score: 38.468180798346204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade the performance of these models. Improving the robustness of code generation models is crucial to better user experience when these models are deployed in real-world applications. However, existing efforts have not addressed this issue for code generation models. To fill this gap, we propose CodeFort, a framework to improve the robustness of code generation models, generalizing a large variety of code perturbations to enrich the training data and enabling various robust training strategies, mixing data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, all carefully designed to support high-throughput training. Extensive evaluations show that we improve the average robust pass rates of baseline CodeGen models from 14.79 to 21.74. Notably, the improvement in robustness against code-syntax perturbations is evidenced by a significant decrease in pass rate drop from 95.04% to 53.35%

Related papers

• Lightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training [0.1533068702686808]
  Small-scale language models can achieve, and even surpass, the performance of larger counterparts in content moderation tasks.<n>This is accomplished through high-fidelity synthetic data generation and adversarial training.
  arXiv  Detail & Related papers  (2025-07-11T03:17:58Z)
• Towards A Generalist Code Embedding Model Based On Massive Data Synthesis [35.04242699869519]
  We introduce textbfCodeR (underlineCode underlineRetrieval), a state-of-the-art embedding model for general-purpose code retrieval.<n>The superior performance of CodeR is built upon CodeR-Pile, a large-scale synthetic dataset constructed under the DRU principle.
  arXiv  Detail & Related papers  (2025-05-19T04:37:53Z)
• AKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks [4.757470449749877]
  This paper introduces Adversarial Knowledge Distillation (AKD) to distill the capabilities of larger models into smaller, more efficient ones.<n>AKD provides a framework for enhancing model robustness, reliability, and security while improving their parameter-efficiency.
  arXiv  Detail & Related papers  (2025-05-05T22:41:19Z)
• Enhancing Variational Autoencoders with Smooth Robust Latent Encoding [54.74721202894622]
  Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models. We introduce Smooth Robust Latent VAE, a novel adversarial training framework that boosts both generation quality and robustness. Experiments show that SRL-VAE improves both generation quality, in image reconstruction and text-guided image editing, and robustness, against Nightshade attacks and image editing attacks.
  arXiv  Detail & Related papers  (2025-04-24T03:17:57Z)
• Iterative Self-Training for Code Generation via Reinforced Re-Ranking [5.77678027975395]
  We propose a novel iterative self-training approach for self-training reranker models using Proximal Policy Optimization (PPO) Unlike traditional PPO approaches, our approach emphasizes the development of a robust reward/reranking model. Our method refines the training dataset by re-evaluating outputs, identifying high-scoring negative examples, and incorporating them into the training loop.
  arXiv  Detail & Related papers  (2025-04-13T16:34:17Z)
• Boosting Alignment for Post-Unlearning Text-to-Image Generative Models [55.82190434534429]
  Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. This often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. We propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives.
  arXiv  Detail & Related papers  (2024-12-09T21:36:10Z)
• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• Scalable Ensembling For Mitigating Reward Overoptimisation [24.58937616758007]
  Reinforcement Learning from Human Feedback has enabled significant advancements within language modeling for powerful, instruction-following models. The alignment of these models remains a pressing challenge as the policy tends to overfit the learned proxy" reward model past an inflection point of utility.
  arXiv  Detail & Related papers  (2024-06-03T05:46:53Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• ReCode: Robustness Evaluation of Code Generation Models [90.10436771217243]
  We propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt.
  arXiv  Detail & Related papers  (2022-12-20T14:11:31Z)
• Adversarial Fine-tune with Dynamically Regulated Adversary [27.034257769448914]
  In many real-world applications such as health diagnosis and autonomous surgical robotics, the standard performance is more valued over model robustness against such extremely malicious attacks. This work proposes a simple yet effective transfer learning-based adversarial training strategy that disentangles the negative effects of adversarial samples on model's standard performance. In addition, we introduce a training-friendly adversarial attack algorithm, which facilitates the boost of adversarial robustness without introducing significant training complexity.
  arXiv  Detail & Related papers  (2022-04-28T00:07:15Z)
• Improving Non-autoregressive Generation with Mixup Training [51.61038444990301]
  We present a non-autoregressive generation model based on pre-trained transformer models. We propose a simple and effective iterative training method called MIx Source and pseudo Target. Our experiments on three generation benchmarks including question generation, summarization and paraphrase generation, show that the proposed framework achieves the new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-10-21T13:04:21Z)
• Non-Autoregressive Translation by Learning Target Categorical Codes [59.840510037250944]
  We propose CNAT, which learns implicitly categorical codes as latent variables into the non-autoregressive decoding. Experiment results show that our model achieves comparable or better performance in machine translation tasks.
  arXiv  Detail & Related papers  (2021-03-21T14:12:34Z)
• Voting based ensemble improves robustness of defensive models [82.70303474487105]
  We study whether it is possible to create an ensemble to further improve robustness. By ensembling several state-of-the-art pre-trained defense models, our method can achieve a 59.8% robust accuracy.
  arXiv  Detail & Related papers  (2020-11-28T00:08:45Z)

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.