nuojohnchen/XtraGPT-7B-SFTed-w_o-Context

XtraGPT-7B-SFTed-w/o-Context

This is an ablation variant of XtraGPT-7B, fine-tuned without paper context in the training data. It is used to demonstrate the importance of training with full paper context for the paper revision task.

Purpose

In the XtraGPT framework, the model receives the full paper content as context when revising a specific section. This ablation model was trained with the same data and procedure, except that the paper context (i.e., <PAPER_CONTENT>...</PAPER_CONTENT>) was removed from all training samples. Only the selected content to be revised was provided during training.

This model serves as evidence that context-aware training is critical for high-quality academic paper revision.

Case Study: Conclusion Revision

Below is a representative example from the conclusion section of the paper "Towards Robust Unlearnable Examples via Deep Hiding", illustrating the difference between the full XtraGPT-7B model (with context) and this ablation model (without context).

Original Text (to revise)

In this paper, we have proposed a novel Deep Hiding scheme that effectively generates unlearnable examples to protect data privacy. Our method utilizes an Invertible Neural Network (INN) to invisibly hide semantic images within clean images, thereby creating deceptive perturbations. The extensive experiments conducted on CIFAR-10, CIFAR-100, and ImageNet-subset demonstrate that our approach outperforms existing methods in terms of robustness against various countermeasures. Overall, our findings indicate that the proposed method significantly enhances the security of sensitive data against unauthorized access.

XtraGPT-7B Full (with context)

In this paper, we introduced a novel Deep Hiding scheme that not only generates unlearnable examples but also significantly enhances data privacy protection. By employing an Invertible Neural Network (INN), our method effectively invisibly integrates semantic images into clean images, creating deceptive perturbations that are robust against various countermeasures. Our extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-subset reveal that our approach achieves an impressive average test accuracy of 16.31%, 6.47%, and 8.15%, respectively, outperforming state-of-the-art methods such as EM and REM, which yield 33.82%, 20.62%, and 22.89%. These results underscore the effectiveness of our method in maintaining the integrity of sensitive data while providing a secure solution against unauthorized access.

XtraGPT-7B-SFTed-w/o-Context (this model)

In this paper, we introduced a novel Deep Hiding scheme designed to generate unlearnable examples for enhanced data privacy protection. By employing an Invertible Neural Network (INN), our method effectively conceals semantic images within clean images, creating deceptive perturbations that are imperceptible to human observers. Our extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-subset reveal that our approach not only surpasses existing methods in robustness against various countermeasures but also maintains high fidelity in image quality. These results underscore the significant potential of our method to enhance the security of sensitive data against unauthorized access, marking a substantial advancement in the field of data privacy.

Analysis

The original conclusion only vaguely states that the method "outperforms existing methods" without any quantitative evidence. The full XtraGPT-7B model, having access to the paper's experimental sections, extracts six precise numerical results (16.31%, 6.47%, 8.15% vs. 33.82%, 20.62%, 22.89%) and names the specific baseline methods (EM, REM), producing a conclusion grounded in concrete evidence. In contrast, this ablation model (without context) can only rephrase the original text with generic expressions like "surpasses existing methods" and "maintains high fidelity," unable to incorporate any substantive experimental findings. This demonstrates that paper context is essential for the model to synthesize key results into the conclusion, rather than merely paraphrasing the surface text.