Introduction
The recently launched chatbot service from OpenAI, ChatGPT, has been gaining widespread attention. Though extensively assessed in numerous aspects, its toughness, most notably its capacity to tolerate unforeseen inputs, stays a riddle. When introducing AI in security-related systems, it is critical to comprehend a model’s stability. We examine how ChatGPT responds to unexpected and unforeseen challenges in this article.
Assessing Adversarial Robustness with AdvGLUE and ANLI Benchmarks.
We employ the AdvGLUE and ANLI benchmarks to test ChatGPT’s vulnerability to adversarial examples. Our assessment showed that ChatGPT is better than other models for most adversarial and OOD tasks. Though it shows promising results, it is crucial to recognize that it is not faultless, suggesting that adversarial and out-of-distribution robustness remain significant difficulties for foundation models.
The performance of the Flipkart review and the DDXPlus medical diagnosis datasets when it is out of distribution is studied.
To evaluate ChatGPT’s OOD performance, we explore the Flipkart review dataset and the DDXPlus medical diagnosis dataset. This information proves ChatGPT’s exceptional skills in handling text conversations. In contrast, when medical tasks are encountered, it prefers providing informal suggestions over definite answers. To ensure dependable application in critical domains, this observation needs to be explored further.
A comparison between ChatGPT’s functionality and that of prominent base models
For our evaluation, we measure ChatGPT against popular reference models. Though ChatGPT’s performance in several classification and translation tasks is above average, it does not accomplish perfection. By comprehending the weak and strong points of the properties, the customization of the model to suit particular uses can be accomplished.
The influence of Responsible AI and safety-critical applications
Evaluation of ChatGPT’s robustness is crucial for developing AI and ensuring safety in critical applications. AI’s expansion and participation in various sectors need understanding the risks and limitations of AI models to guarantee their moral and secure deployment.
Dialogue Understanding Capabilities of ChatGPT
Dialog-based text is ChatGPT’s expertise. Achieving this competency could enhance human computer interactions and improve user experience. Strict ethical norms are therefore required because of this technology.
Informal Suggestions in Medical Tasks: A Critical Observation
Our analysis reveals a fascinating feature of ChatGPT’s behaviour in medical tasks. While providing medical advice, ChatGPT usually offers informal suggestions, which can be problematic in some serious health conditions. This illustration shows that it is important to manage AI systems’ output precisely in sensitive areas.
Future Research Directions and Conclusions
ChatGPT has great potential but struggles to handle adversarial inputs and out-of-distribution samples. The content of this article establishes a basis for more investigations and illustrates the importance of pursuing AI technology responsibly.
Our analysis of ChatGPT’s versatility offers invaluable insights into the advantages and shortcomings of this sophisticated technology. AI’s complexity necessitates the responsible usage of it for a secure and dependable future
Reference
