On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective (2302.12095v5)

Abstract: ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.

• The paper demonstrates that while ChatGPT outperforms many models on adversarial tasks using benchmarks like AdvGLUE and ANLI, it still exhibits significant vulnerabilities at both word and sentence perturbation levels.
• The paper reveals that ChatGPT shows strong out-of-distribution performance, notably in domain-specific tests such as DDXPlus and Flipkart reviews, yet its F1 scores indicate room for improvement with unfamiliar data.
• The paper underscores the need for advanced adversarial training and ethical safeguards to enhance model robustness for safety-critical applications.

An Expert Analysis on the Robustness Evaluation of ChatGPT

The paper, titled "On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective," presents a detailed empirical analysis of ChatGPT, focusing on its response to adversarial and out-of-distribution (OOD) inputs. This paper is grounded in understanding the robustness of ChatGPT, which is crucial for deploying this LLM in real-world, safety-critical applications.

Key Insights from the Study

The paper explores ChatGPT's robustness using two main evaluation frameworks: AdvGLUE and ANLI for adversarial inputs, and Flipkart reviews and DDXPlus for OOD inputs. The paper further contrasts ChatGPT with other foundational models, including both commercially available models such as OpenAI's text-davinci series and open-source models from the Huggingface hub, to provide a comparative performance landscape.

Adversarial Robustness

1. Evaluation on AdvGLUE and ANLI:
   • The paper employs the AdvGLUE benchmark, which is modified from the GLUE tasks to include adversarial perturbations at both the word and sentence level, covering tasks like SST-2 sentiment analysis and MNLI.
   • Results demonstrate that while ChatGPT outperforms many counterparts in resilience to adversarial attacks, its absolute performance remains unsatisfactory, indicating vulnerabilities.
2. Significance of Word and Sentence-Level Perturbations:
   • Word-level perturbations, such as typos, and sentence-level manipulations, known as distractions, exhibited a substantial impact on ChatGPT's decision-making capabilities.
   • A critical observation was ChatGPT's inconsistent performance across different task-specific datasets, underscoring the complexity of adversarial defense in NLP.

Out-of-Distribution Robustness

1. Performance across Flipkart and DDXPlus:
   • ChatGPT illustrated a commendable performance on OOD classifications, particularly excelling in understanding dialogue and domain-specific contexts evident from the DDXPlus medical diagnosis dataset.
   • Despite its performance advantage, the absolute F1 scores suggest scope for improvement, especially when faced with less familiar data domains.
2. Comparison with Other Foundation Models:
   • ChatGPT showcased competitive performance against established models like Flan-T5-L and text-davinci-003. The paper highlighted that scaling model parameter sizes and incorporating instruction tuning can correlate positively with robustness in unseen conditions.

Machine Translation Tasks

• The paper extends the robustness evaluation to adversarial text translation, using an English-to-Chinese dataset derived from AdvGLUE. ChatGPT's translations were coherent and robust against adversarial noise, though it was outperformed by text-davinci-003 in specific metrics like BLEU.

Broader Implications and Future Directions

The paper's findings have several implications for the development and deployment of LLMs:

1. Theoretical and Practical Challenges:
   • The results underscore the necessity of advancing theoretical understanding and methodologies to reinforce robustness in LLMs against adversarial perturbations. A balanced approach involving data augmentation and improved training regimens, including adversarial training, appears imperative.
2. Ethical Considerations:
   • The paper raises ethical considerations regarding the responsible deployment of LLMs in sensitive domains such as healthcare, emphasizing the need for models that provide not only strong performance but also reliable and safe outputs.
3. Foundation Models Beyond NLP:
   • The results and methodologies discussed in the paper could spark broader research exploration beyond NLP, extending into fields such as computer vision and multimodal learning.
4. Open Questions in Robustness:
   • Despite high in-distribution performance, the explorations question whether current LLM architectures can naturally achieve OOD robustness due merely to parameter scale and training data volume.

This paper's insights and findings form a valuable contribution to the ongoing discourse on LLM robustness, presenting practical recommendations while opening pathways for future exploration in this complex field. As LLMs continue to grow in applicability, ensuring their robustness remains a critical challenge for the research community.