In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT (2304.08979v2)

Abstract: The way users acquire information is undergoing a paradigm shift with the advent of ChatGPT. Unlike conventional search engines, ChatGPT retrieves knowledge from the model itself and generates answers for users. ChatGPT's impressive question-answering (QA) capability has attracted more than 100 million users within a short period of time but has also raised concerns regarding its reliability. In this paper, we perform the first large-scale measurement of ChatGPT's reliability in the generic QA scenario with a carefully curated set of 5,695 questions across ten datasets and eight domains. We find that ChatGPT's reliability varies across different domains, especially underperforming in law and science questions. We also demonstrate that system roles, originally designed by OpenAI to allow users to steer ChatGPT's behavior, can impact ChatGPT's reliability in an imperceptible way. We further show that ChatGPT is vulnerable to adversarial examples, and even a single character change can negatively affect its reliability in certain cases. We believe that our study provides valuable insights into ChatGPT's reliability and underscores the need for strengthening the reliability and security of LLMs.

• The paper evaluates ChatGPT's reliability in diverse QA scenarios, revealing performance differences across domains such as law and science.
• It measures the effect of system roles, showing benign roles increase correctness while adversarial roles significantly decrease performance.
• The study finds ChatGPT struggles with unanswerable questions and adversarial attacks, highlighting critical vulnerabilities in its methodology.

Measuring and Characterizing the Reliability of ChatGPT

The paper "In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT" provides a comprehensive analysis of ChatGPT's reliability in question-answering (QA) contexts. This examination is critical given the widespread adoption of ChatGPT, which has rapidly amassed over 100 million users. The paper scrutinizes the model's performance across different knowledge domains, assesses the impact of system roles on its reliability, and evaluates its robustness against adversarial examples.

Evaluation of ChatGPT’s Reliability

The authors conduct a large-scale empirical paper involving 5,695 questions sourced from ten datasets covering eight domains, including history, law, and technology. Their goal is to answer three primary questions: how reliable ChatGPT is in generic QA scenarios, whether system roles affect its reliability, and how it performs against adversarial examples.

1. Reliability in Various Domains: The research highlights variability in ChatGPT’s performance across different domains. While showing relatively high correctness in recreation and technology questions, ChatGPT underperformed significantly in the law and science domains. For instance, its correctness for law questions was observed to be over 11% lower than the overall average for extractive tasks.
2. Correctness vs. Unanswerable Question Detection: A key reliability facet is the ability to detect unanswerable questions. Alarmingly, ChatGPT fails to adequately identify these questions, with an unanswerable detection rate of only 26.63% for GPT-3.5, which drops to 14.29% for GPT-4. This is critical as failing to recognize unanswerable queries can lead to misinformation dissemination.
3. Impacts of System Roles: The paper also explores how different system roles, such as 'Expert' roles and adversarial intentions like 'jailbreak' roles, can systematically alter ChatGPT's reliability. Interestingly, benign system roles enhance correctness across all QA tasks, demonstrating an increase of over 3% in some datasets, while jailbreak and poorly constructed roles decrease reliability.
4. Adversarial Robustness: ChatGPT's susceptibility to adversarial examples is a significant concern. The paper indicates that adversarial attacks, even simple ones like character swaps or paraphrasing, can dramatically reduce response accuracy. The vulnerability is particularly pronounced with character-level attacks, prompting questions about ChatGPT's robustness and potential security weaknesses.

Methodological Insights and Future Directions

The analysis carried out in the paper involves thematic categorization and rigorous testing using various adversarial techniques. The findings prescribe enhanced methodological frameworks for future evaluations of LLMs.

• Data Quality and System Role Exploration: Improving training data quality is crucial to enhance ChatGPT’s reliability. Furthermore, systematic exploration and development of reliable system roles are vital, as these roles significantly affect task execution and reliability.
• In-depth Adversarial Training: Strengthening adversarial defenses could make ChatGPT more robust. The insights provided by the paper's adversarial analyses suggest the necessity for more nuanced training regimes that can prepare models to handle adversarially perturbed inputs more effectively.

The paper's insights underscore the importance of rigorous reliability evaluations for AI systems broadly used for information dissemination. The research is pivotal in highlighting key areas where ChatGPT and similar models must improve to ensure they can be trusted for critical applications spanning legal, scientific, and medical domains. These efforts are necessary to safeguard public trust in AI technologies and to realize their potential fully across various sectors.