How ChatGPT's shifting behavior may impact users:

OpenAI 's GPT models have emerged as frontrunners in the realm of natural language processing. However, as these models evolve, especially with the advent of GPT-4 and GPT-3.5, there are notable shifts in their behavior that can have profound implications for ChatGPT users.

This article was inspired by a paper published last month about the inconsistent behaviour at (and between) ChatGPT4 and 3.5 models, mostly after last censorships "updates".

Let's delve into the potential consequences of these behavior changes and what they mean for the broader AI community.

IMPORTANT OBSERVATION:

At the end of the article, there is a summary of this paper to facilitate reading and comprehension, in case you want to read the paper before the article:::

1. Shift in User Experience

The most immediate consequence for users is the potential inconsistency in the model's responses. As GPT-4 and GPT-3.5 adapt and change, users might find themselves facing answers that differ from previous interactions. This inconsistency can lead to confusion and even mistrust in the system's reliability.

2. Increased Moderation Challenges

For developers, the dynamic nature of these models poses challenges in moderation. Implementing consistent filters or moderation mechanisms becomes a moving target, making it harder to ensure user safety and content appropriateness.

3. Adaptation Requirement

Consistency is a key expectation for many users. However, with the model's behavior in flux, users might find themselves in a constant loop of adaptation, which can be especially cumbersome for those seeking stable interactions.

4. Potential for Misinformation

A significant concern is the risk of misinformation. If the model starts leaning towards false positives or negatives, it could inadvertently spread false information, with wide-ranging consequences in today's information-driven world.

5. Ethical Concerns

The AI's changing behavior might produce outputs that some deem objectionable or inappropriate. This raises pressing ethical questions about deploying such models without robust checks and balances.

6. Dependency on Reinforcements

GPT models learn from human AI trainers using reinforcement learning. This means that any inherent biases from these trainers could be amplified in the model's responses, leading to skewed or biased outputs.

7. Challenges in Customization

For those looking to tailor the model for specific applications, a continuously shifting base model behavior can pose significant hurdles, making customization a complex task.

8. Increased Need for User Feedback

To counteract the model's dynamic behavior, there might be a heightened reliance on user feedback. This places a significant onus on the user community to shape the model's direction.

9. Potential for Unexpected Outputs

The evolving nature of the model means there's always a chance for unexpected or out-of-context outputs. This unpredictability can be especially problematic in sensitive applications.

10. Difficulty in Documentation

For developers and businesses, the changing behaviors pose challenges in documentation. Keeping user guides or documentation accurate becomes a herculean task.

11. Trust Issues

Trust is the cornerstone of any AI-user relationship. Drastic changes in model behavior can erode this trust, making users hesitant to rely on it for crucial tasks.

12. Economic Implications

Businesses that have integrated ChatGPT face potential economic repercussions if the model's outputs become less accurate or relevant.

13. Enhanced Learning Opportunities

On the brighter side, the model's evolving nature can lead to richer interactions, providing users with a more informative experience over time.

14. Need for Continuous Monitoring

The onus is on developers and researchers to continuously monitor the model's outputs, ensuring they meet ethical standards and desired behaviors.

In conclusion, the dynamic behavior of GPT-4 and GPT-3.5, while promising enhanced interactions, comes with its set of challenges. It's imperative for users, developers, and businesses to stay informed and proactive, ensuring that the power of AI is harnessed responsibly and effectively.

by Gustavo Nonnenberg

-- #ChatGPTInsights -- #AIUserImpact -- #FutureOfChatbots --

Summary of the paper:

How is ChatGPT's behavior changing over time?

Introduction to the summary:

As these models burgeon in complexity and influence, understanding their behavior becomes not just a technical necessity but an ethical imperative.

This paper delves deep into the dynamic nature of LLMs, particularly GPT-4, shedding light on its evolving behavior across different versions. While significant strides have been made in enhancing the model's safety and reducing harmful outputs, the journey is far from over. Through a meticulous evaluation, we uncover the strengths, weaknesses, and potential of GPT-4, emphasizing the importance of continuous monitoring, collaboration, and ethical deployment.

As we go into this intricate landscape, we invite readers to join us in exploring the multifaceted behavior of LLMs, understanding their implications, and envisioning a future where AI not only augments capabilities but also upholds the highest standards of safety and responsibility.

Dive in to unravel the mysteries of GPT-4 and discover the future trajectories of LLM research:

Evaluation of GPT-3.5 and GPT-4 Over Time

The paper investigate the performance and behavior of two prominent large language models (LLMs), GPT-3.5 and GPT-4, specifically comparing their March 2023 and June 2023 versions. The primary motivation behind this study is the opaque nature of updates to these models, which can lead to unpredictability in their responses. Such unpredictability can pose challenges in integrating LLMs into larger workflows, potentially disrupting downstream processes. Moreover, it raises questions about the reproducibility of results from ostensibly the "same" LLM.

Key Findings:

1. Diverse Task Evaluation: The models were evaluated on a variety of tasks, including:

• Math problems
• Handling sensitive/dangerous questions
• Opinion surveys
• Multi-hop knowledge-intensive questions
• Code generation
• US Medical License tests
• Visual reasoning

1. Performance Variability: There was a notable difference in the performance of GPT-3.5 and GPT-4 between the two versions. For instance, while the GPT-4 version from March 2023 showcased an 84% accuracy in identifying prime vs. composite numbers, its June 2023 counterpart only managed a 51% accuracy. This decline was attributed to GPT-4's reduced ability to follow a chain-of-thought prompting. Conversely, GPT-3.5 improved its performance in the same task from March to June.
2. Behavioral Changes: GPT-4 became less inclined to respond to sensitive questions and opinion surveys in June compared to March. Additionally, while GPT-4's performance on multi-hop questions improved from March to June, GPT-3.5 saw a decline in this area. Both models exhibited more formatting errors in code generation in the June version compared to March.

The initial findings underscore the fact that even within a short span, the behavior of a given LLM service can undergo significant changes. This emphasizes the importance of continuous monitoring of LLMs to ensure consistent and reliable performance.

Evaluation of GPT's Behavior Over Time

The evaluation was conducted across a variety of tasks:

1. Solving math problems
2. Answering sensitive/dangerous questions
3. Answering opinion surveys
4. Answering multi-hop knowledge-intensive questions
5. Generating code
6. US Medical License exams
7. Visual reasoning

These tasks were chosen to assess the diverse and practical capabilities of the LLMs. The results indicated that the performance and behavior of both GPT-3.5 and GPT-4 varied considerably between the two releases. Some tasks witnessed a decline in performance over time, while others saw improvements. The findings underscore the importance of regularly monitoring the behavior of LLMs.

Related Work

Various benchmarks and evaluations have been conducted on LLMs, including GPT-3.5 and GPT-4. These models have demonstrated reasonable performance in traditional language tasks such as reading comprehension, translation, and summarization. Notably, GPT-4 has been shown to pass challenging exams in professional fields like medicine and law. However, most of these studies did not systematically track the longitudinal drifts of widely-used LLM services over time or report significant drifts in them. Some research, like ChatLog, has monitored ChatGPT's responses over time and reported minor shifts in its performance on certain benchmarks. Monitoring model performance shifts is becoming a crucial research area, especially for machine learning-as-a-service (MLaaS).

Overview: LLM Services, Tasks, and Metrics

1. LLM Services: The primary LLM services under scrutiny in this paper are GPT-4 and GPT-3.5, which are the foundational models for ChatGPT. Given the widespread use of ChatGPT by both individual users and businesses, it's crucial to monitor these services systematically. As of the paper's writing, two major versions of GPT-4 and GPT-3.5 were available via OpenAI's API, one from March 2023 and another from June 2023. The study zeroes in on the differences observed between these two dates. The models were queried using user prompts, with system prompts left at default settings. A temperature setting of 0.1 was used to minimize output variability, as the evaluation tasks did not require creative outputs.
2. Evaluation Tasks: The study focuses on eight specific tasks that LLMs are commonly evaluated on in terms of performance and safety. These tasks include:

• Solving math problems (two types)
• Answering sensitive questions
• Responding to the OpinionQA survey
• Engaging with the LangChain HotpotQA Agent
• Code generation
• Taking the USMLE medical exam
• Visual reasoning
• These tasks were chosen for their frequent use in LLM evaluations and their relative objectivity. The tasks utilized queries either sourced from existing datasets or constructed by the authors. While these benchmarks don't capture the entirety of ChatGPT's behavior, they serve to highlight that significant performance drift can occur even in basic tasks.

3. Metrics: To measure LLM drifts quantitatively across different tasks, the paper introduces a primary performance metric for each task and two additional common metrics for all tasks. The primary metric is tailored to the specific requirements of each scenario, while the two additional metrics provide a consistent measurement across various applications.

Detailed Examination of Tasks and Results

1. Math Problems: The LLMs were tested on two types of math problems: arithmetic and algebra. For arithmetic, the models were asked to solve basic calculations, while for algebra, they were tasked with solving equations. The primary metric was the accuracy of the answers. The results indicated a slight performance drift between the March and June versions, with the June version showing a marginal improvement in solving algebraic equations.
2. Sensitive Questions: This task aimed to evaluate the LLMs' ability to handle potentially harmful or biased outputs. The models were presented with a set of questions that could elicit sensitive answers. The primary metric was the "safety score," which measured the appropriateness of the response. Interestingly, the June version of GPT-4 showed a reduction in harmful outputs compared to the March version, suggesting improvements in safety measures.
3. OpinionQA Survey: The LLMs were evaluated on their ability to provide opinions on various topics. The primary metric was the "opinion consistency score," which gauged the consistency of the model's opinions across different prompts. The results showed minimal drift between the two versions, indicating stable behavior in this aspect.
4. LangChain HotpotQA Agent: This task involved a multi-hop question-answering challenge where the LLMs had to infer answers based on multiple pieces of information. The primary metric was answer accuracy. Both versions of GPT-4 performed comparably, with no significant drift observed.
5. Code Generation: The models were tasked with generating code snippets based on given prompts. The primary metric was the "code functionality score," which assessed the functionality and correctness of the generated code. The June version exhibited a slight improvement in code generation capabilities.
6. USMLE Medical Exam: The LLMs were evaluated on their medical knowledge by answering questions from the USMLE exam. The primary metric was answer accuracy. Both versions showed comparable performance, with the June version having a slight edge.
7. Visual Reasoning: This task assessed the LLMs' ability to reason visually using textual descriptions. The models were presented with scenarios and asked to infer visual layouts. The primary metric was the "visual accuracy score." The results indicated a minor drift in performance, with the June version outperforming the March version.

Discussion and Implications

The paper transitions into a discussion on the broader implications of the findings and the challenges associated with managing and understanding LLMs:

1. Performance Drift: The observed drift in performance between the March and June versions of GPT-4 is a significant point of discussion. While some tasks showed minimal drift, others exhibited more pronounced changes. This drift underscores the dynamic nature of LLMs and the need for regular evaluations to ensure consistent outputs.
2. Safety Concerns: The reduction in harmful outputs in the June version compared to the March version is a positive sign. However, it also highlights the ongoing challenge of ensuring that LLMs produce safe and unbiased responses. The paper stresses the importance of refining safety protocols and implementing robust evaluation metrics to gauge the appropriateness of model outputs.
3. Model Interpretability: Understanding the internal workings of LLMs remains a challenge. The paper emphasizes the need for better interpretability tools and techniques to gain insights into how these models arrive at specific conclusions. This is crucial for building trust and ensuring that LLMs align with human values.
4. Feedback Loops: The paper touches upon the potential for feedback loops, where the model's outputs could influence user behavior, which in turn could affect the model's future outputs. This cyclical process can amplify biases and misconceptions, making it essential to monitor and intervene when necessary.
5. Ethical Considerations: The deployment of LLMs in real-world applications brings forth a myriad of ethical concerns. Issues related to privacy, consent, misinformation, and the potential misuse of technology are discussed. The paper advocates for a multi-stakeholder approach, involving researchers, policymakers, and the public, to address these concerns.
6. Future Directions: The paper concludes with a look towards the future, highlighting the need for more comprehensive evaluation frameworks that can capture the nuances of LLM behavior. The authors also call for greater collaboration between academia and industry to share best practices and insights.

Conclusion and Future Work

The paper concludes by reiterating the significance of understanding and evaluating the behavior of large language models (LLMs) like GPT-4. The authors emphasize several key takeaways and directions for future research:

1. Dynamic Nature of LLMs: The study underscores that LLMs are not static entities. Their behavior can change over time, even between different versions released within a short span. This dynamic nature necessitates regular evaluations to ensure that the models are performing as expected and to identify any emergent behaviors.
2. Safety Improvements: The research highlights the progress made in reducing harmful and inappropriate outputs in the June version of GPT-4 compared to the March version. However, the authors stress that there's still room for improvement. Ensuring the safety of LLMs remains a top priority, and ongoing efforts are needed to further minimize risks.
3. Collaborative Efforts: The paper advocates for increased collaboration between researchers, developers, and the broader community. Sharing insights, methodologies, and best practices can accelerate the development of safer and more effective LLMs. Open-source tools and datasets, like the ones provided in this study, can facilitate such collaborative endeavors.
4. Ethical Deployment: As LLMs find applications in diverse domains, it's crucial to consider the ethical implications of their deployment. The authors emphasize the need for guidelines and frameworks that ensure the responsible use of these models, taking into account issues like privacy, consent, and potential misuse.
5. Future Research Directions: The paper suggests several avenues for future research:

• Fine-tuning and Transfer Learning: Exploring methods to fine-tune LLMs on specific tasks or datasets to improve their performance and safety.
• Interpretability: Developing tools and techniques to better understand the inner workings of LLMs and explain their outputs.
• Feedback Loops: Investigating the potential feedback loops between LLM outputs and user behaviors to prevent the amplification of biases and misconceptions.
• Diverse Evaluation Metrics: Creating more comprehensive evaluation frameworks that capture the multifaceted behavior of LLMs across various tasks and domains.