Chatbot performance degradation: Data challenges threaten to generate the future of artificial intelligence

This article briefly:

Research has shown that the performance of chatbots such as ChatGPT may degrade over time due to the degradation of training data quality.

· Machine learning models are susceptible to data poisoning and model crashes, which can significantly reduce the quality of their output.

A reliable source of content is critical to preventing chatbot performance degradation, creating challenges for future AI developers.

Modern chatbots are constantly learning and their behavior is always changing, but their performance may decrease or improve.

Recent research overturns the assumption that “learning always means progress,” which has implications for the future of ChatGPT and its peers. To keep chatbots up and running, artificial intelligence (AI) developers must address emerging data challenges.

ChatGPT is getting dumber over time

A recently published study suggests that chatbots may be less able to perform certain tasks over time.

To reach this conclusion, the researchers compared the output of the large language model (LLM) GPT-3.5 and GPT-4 in March and June 2023. In just three months, they observed dramatic changes in the models underpinning ChatGPT.

For example, in March of this year, GPT-4 was able to identify prime numbers with 97.6% accuracy. By June, its accuracy had plummeted to 2.4 percent.

GPT-4 (left) and GPT-3.5 (right) responses to the same question in March and June (source: arXiv)

The experiment also assessed the model's speed at answering sensitive questions, its ability to generate code, and its ability to reason visually. Across all the skills they tested, the team observed that the quality of AI output deteriorated over time.

Challenges with real-time training data

Machine learning (ML) relies on a training process whereby AI models can mimic human intelligence by processing large amounts of information.

For example, the development of the LLM that powers modern chatbots has benefited from the availability of a large number of online repositories. These include datasets compiled from Wikipedia articles, enabling chatbots to learn by digesting the largest body of human knowledge ever created.

But now, tools like ChatGPT are widely released. Developers have much less control over their ever-changing training data.

The problem is that such models can also "learn" to give wrong answers. If the quality of the training data degrades, their output will also degrade. This poses a challenge for dynamic chatbots, which require a steady stream of web scraped content.

Data poisoning can lead to chatbot performance degradation

Because chatbots tend to rely on content scraped from the web, they are particularly vulnerable to a type of manipulation known as data poisoning.

That's exactly what happened to Microsoft's Twitter bot Tay in 2016. Less than 24 hours after its launch, ChatGPT's predecessor began posting inflammatory and offensive tweets. Microsoft developers quickly paused it and started over.

As it turns out, cyber trolls spam the bot from the start, manipulating its ability to learn from its interactions with the public. After being abused by the 4channer army, it’s no surprise that Tay started parroting their hate speech.

Like Tay, contemporary chatbots are a product of their environment and are vulnerable to similar attacks. Even Wikipedia, so important in the development of the LLM, could be used to poison machine learning training data.

However, intentionally corrupted data is not the only source of misinformation that chatbot developers need to be wary of.

**Model Crash: Time Bomb for Chatbots? **

With the growing popularity of AI tools, AI-generated content is also proliferating. But what happens to LL.M.s trained on web scraping datasets if more and more content is itself created by machine learning?

This question was explored in a recent survey of the impact of recursion on machine learning models. The answers it finds have major implications for the future of generative artificial intelligence.

Researchers found that when AI-generated material was used as training data, machine-learning models began to forget what they had previously learned.

They coined the term "model collapse," noting that different AI families all tend to degenerate when exposed to human-created content.

In one experiment, the team created a feedback loop between an image-generating machine learning model and its output.

After observation, they found that after each iteration, the model amplified its own mistakes and began to forget the data that was originally generated by humans. After 20 loops, the output is almost similar to the original dataset.

The output of an image generation ML model (source: arXiv)

The researchers observed the same degradation trend when performing a similar scenario with LL.M. Also, with each iteration, errors such as repeated phrases and broken speech occur more frequently.

Accordingly, the study speculates that future generations of ChatGPT may be at risk of model collapse. If AI generates more and more online content, the performance of chatbots and other generative machine learning models could deteriorate.

Reliable content you need to prevent chatbot performance degradation

Going forward, reliable content sources will become increasingly important to prevent the degrading effects of low-quality data. Those companies that control access to what is needed to train machine learning models hold the key to further innovation.

After all, it's no coincidence that tech giants with millions of users are big names in artificial intelligence.

In the last week alone, Meta released the latest version of LLM Llama 2, Google rolled out new features for Bard, and there were reports that Apple was preparing to enter the fray.

Whether driven by data poisoning, early signs of model breakdown, or other factors, the threat of performance degradation cannot be ignored by chatbot developers.