OpenAI admits that ChatGPT got lazy!

The speed and performance of the responses has decreased

ChatGPT got lazy

Humans become lazy because they lack motivation

We humans get lazy sometimes to complete our work, while some people don’t recover from that laziness some people get rid of it by making some conscious decisions. The concept of laziness has now crept into Artificial intelligence tools such as ChatGPT.

First let me show you what OpenAI has said in its recent tweet

Tweet from ChatGPT

Now, what does getting lazier mean?

In this context, “getting lazier” likely refers to the perception that the performance of the GPT-4 model has deteriorated or that it’s not responding with the same level of detail, promptness, or relevance as expected.

In human terms, laziness often means a lack of effort or a decrease in productivity. Thus, the comparison suggests that the AI is not “working as hard” as it should be or as it previously did.

This “laziness” might mean

not following the prompt correctly

not remembering the context

not remembering previous responses

not remembering previous prompts

not following the response structure

Now comes the million dollars question..

Why did ChatGPT become LAZY?

From the tweet the OpenAI has implied that last model update was on Nov 11TH . So not updating the model is a clear problem here.

I exactly don’t know the OpenAI’s policies on updating the models and what data they use to update the model, but I think they update the model with new prompt data, new events that took place since the latest update.

But even though the model has not been updated with new data its performance should be as expected with existing prompts why is it not performing as expected?

The part answer lies in this tweet:

training chat models is not clean industrial process

The training of LLMs is not a clean process, since the large language models have very less explain ability and they are not consistent enough with their performance and characteristics. So every time they respond in different style which might lead to different personality, refusal to follow instructions, forgetting chain of commands and even having political bias.

This problem can also be explained by “dynamic evaluation problem”. It refers to a challenge in the context of machine learning and artificial intelligence, particularly in the area of language models like ChatGPT.

In a broad sense, dynamic evaluation is about continuously adapting or updating a model’s parameters during inference time, based on the new data it encounters. This contrasts with the more traditional static evaluation, where a model’s parameters are fixed after the training phase and do not change during inference.

For language models, the dynamic evaluation problem can be understood in the context of adapting the model’s responses based on ongoing conversation or new information that wasn’t part of its original training data. This involves a few key challenges:

• Real-Time Learning: The model needs to learn or adapt from the new data it encounters in real-time or near-real-time. This is challenging because traditional deep learning models are generally not designed for incremental learning but rather for batch learning from a fixed dataset.
• Memory and Forgetting: A dynamically evaluating model must decide what to remember and what to forget. This involves complex decisions about the relevance and importance of new information.
• Generalization vs. Specialization: The model needs to balance its ability to generalize across a wide range of topics while also specializing or adapting to the specifics of a given conversation or context.
• Computational Efficiency: Dynamic evaluation can be computationally expensive, as it may require ongoing adjustments to a model’s parameters. This needs to be balanced against the need for real-time responsiveness.
• Stability and Drift: There’s a risk that continuous learning could lead to model drift, where the model’s performance degrades over time due to cumulative errors or biases in the new data.

The exact reasons for degradation in performance?

We don’t know! No one knows!! Hell even OpenAI does not know!! the reason for its decrease in performance it can be a cumulative of multiple reasons such as data drift, model drift, challenging prompts or data poisoning and so on..

So what might be the solution for this problem?

Solving this problem is less like updating a website with new features more an artisanal multi-person effort to plan, create, and evaluate a new chat model with new behavior!

To address and fix these problems, the following steps could be taken:

• Model Retraining: Adjust the model with new, curated training data to correct any drift.
• Update Algorithms: Implementing improved algorithms that can handle the unpredictability better.
• User Feedback Analysis: Analyzing user feedback to identify specific problems and adjust the model accordingly.
• Monitoring and Evaluation: Continuous monitoring of the model’s performance and setting up automated systems to alert the team to any degradation in quality.

It’s important to note that solving these problems involves a multifaceted approach, including software engineering, data science, machine learning and user experience expertise. It is not one person or one department problem, it is a collective problem, while training and deploying AI models every department has to do their job right because of their low explain ability its very difficult to pinpoint what caused the problem.

Conclusion

With this incident it is certain that the dream of Artificial General Intelligence (AGI) has taken a step back. It still seems a far-fetched dream since the Artificial Narrow Intelligence(ANI) tools like ChatGPT has a long way to go with its performance, stability and using it for business. It still hasn’t gotten that status to be placed in a business workflow and function autonomously.