Robustness in language interpretation using LLMs

https://arxiv.org/pdf/2309.10644.pdf

I’ve used language models for a while now. They are capable of many tasks, but one of their main problem is the robustness of the results. The models can produce very different results if we change only a minor detail.

This paper addresses the challenge of interpretability in deep learning models used for source code classification tasks such as functionality classification, authorship attribution, and vulnerability detection. The authors propose a novel method called Robin, which aims to create robust interpreters for deep learning-based code classifiers.

Key points from the paper include:

1. Problem with Current Interpretability: The authors note that existing methods for interpreting deep learning models are not robust and struggle with out-of-distribution examples. This is a significant issue because practitioners need to trust the model’s predictions, especially in high-security scenarios.
2. Robin’s Approach: Robin introduces a hybrid structure that combines an interpreter with two approximators. This structure leverages adversarial training and data augmentation to improve the robustness and fidelity of interpretations.
3. Experimental Results: The paper reports that Robin achieves on average a 6.11% higher fidelity when evaluated on the classifier, 67.22% higher fidelity when evaluated on the approximator, and 15.87 times higher robustness compared to existing interpreters. Additionally, Robin is less affected by out-of-distribution examples.
4. Contributions: The paper’s contributions are threefold: addressing the out-of-distribution problem, improving interpretation robustness, and empirically evaluating Robin’s effectiveness compared to known post-hoc methods.
5. Motivating Instance: The authors provide a specific instance of code classification to illustrate the problem inherent to the local interpretation approach, demonstrating the need for a robust interpreter like Robin.
6. Design of Robin: The paper details the design of Robin, which includes generating perturbed examples, leveraging adversarial training, and using mixup to augment the training set.
7. Source Code Availability: The source code for Robin has been made publicly available, which can facilitate further research and application by other practitioners.
8. Paper Organization: The paper is structured to present a motivating instance, describe the design of Robin, present experiments and results, discuss limitations, review related work, and conclude the study.

The authors conclude that Robin is a significant step forward in producing interpretable and robust deep learning models for code classification, which is crucial for their adoption in real-world applications, particularly those requiring high security.