A closer look at the strange 'winter break' behavior of ChatGPT-4

The most popular generative artificial intelligence (AI) is beginning to become "lazy" as the winter approaches. That's the claim of some savvy ChatGPT users.

According to a recent ArsTechnica report in late November, users of ChatGPT, the AI chatbot developed by GPT-4, the natural language model of OpenAI started to notice something unusual. In response to specific requests, GPT-4 refused to finish tasks or provide simplified "lazy" responses instead of the typically precise responses.

OpenAI admitted to the problem but claimed they did not intentionally modify the model. Some now speculate this laziness may be an unintended consequence of GPT-4 emulating seasonal human behavior changes.

It is referred to as"the "winter break hypothesis," it is believed that since GPT-4 is fed by the current time, it has learned from its huge collection of training data that people tend to complete large-scale projects, and then slow down in December. Researchers are currently examining whether this seemingly absurd idea is true. The fact that it's considered serious underscores the ambiguous and human-like nature of large modeling of language (LLMs) like GPT-4.

On the 24th of November, a Reddit user complained that GPT-4 was unable to fill in a huge CSV file however, it only gave one entry as a template. On the 1st of December, OpenAI's Will Depue confirmed awareness of "laziness problems" that are related to "over-refusals" and committed to correcting these issues.

Some say GPT-4 was always sporadically "lazy," and recent reports are not confirmation bias. However, the timing of users noticing more refusals after the November 11th update of GPT-4 Turbo is interesting if coincidental and some assumed it was a new technique to use OpenAI to save on computing.

The "Winter break" theory

On December 9, researcher Rob Lynch found GPT-4 generated 4,086 characters on a December date prompt versus 4,298 when given dates in May. However, even though AI researcher Ian Arawjo couldn't reproduce Lynch's results to a statistically significant degree, the subjective nature of sampling bias associated with LLMs makes reproducibility a challenge. As researchers attempt to understand the theory, it continues to enthrall researchers in the AI community.

Geoffrey Litt of Anthropic, the creator of Claude, described it as "the most entertaining theory of all time," yet admitted it's difficult to determine if it's true because of the many bizarre ways LLMs respond to human-like guidance and prompts as evidenced by the increasingly strange prompts. For example, research shows GPT models result in higher math scores when instructed to "take a deep breath," while the promise of a "tip" prolongs the time to complete. The lack of transparency around possible changes to GPT-4 is what makes even the most unlikely theories worthy of investigation.

This program demonstrates the ambiguity of large-scale language models as well as the new methodologies required to analyze their ever-changing abilities and weaknesses. Also, it demonstrates the global collaboration in progress to assess AI advances that impact society. It also serves as a reminder that our LLMs need a great deal of monitoring and testing before they can be properly used in real-world applications.

 A "winter break-up hypothesis" which is the reason for GPT-4's apparent seasonal slowness could prove untrue or provide insights that can improve future iterations. Whatever the outcome, this intriguing case exemplifies the strangely anthropomorphic aspect of AI systems and the priority of identifying risks and pursuing rapid innovation.