machine learning

In late November, some ChatGPT users began to notice that ChatGPT-4 was becoming more “lazy,” reportedly refusing to do some tasks or returning simplified results. Since then, OpenAI has admitted that it’s an issue, but the company isn’t sure why. The answer may be what some are calling “winter break hypothesis.” While unproven, the fact that AI researchers are taking it seriously shows how weird the world of AI language models has become.

“We’ve heard all your feedback about GPT4 getting lazier!” tweeted the official ChatGPT account on Thursday. “We haven’t updated the model since Nov 11th, and this certainly isn’t intentional. model behavior can be unpredictable, and we’re looking into fixing it.”

On Friday, an X account named Martian openly wondered if LLMs might simulate seasonal depression. Later, Mike Swoopskee tweeted, “What if it learned from its training data that people usually slow down in December and put bigger projects off until the new year, and that’s why it’s been more lazy lately?”

Since the system prompt for ChatGPT feeds the bot the current date, people noted, some began to think there may be something to the idea. Why entertain such a weird supposition? Because research has shown that large language models like GPT-4, which powers the paid version of ChatGPT, respond to human-style encouragement, such as telling a bot to “take a deep breath” before doing a math problem. People have also less formally experimented with telling an LLM that it will receive a tip for doing the work, or if an AI model gets lazy, telling the bot that you have no fingers seems to help lengthen outputs.

On Monday, a developer named Rob Lynch announced on X that he had tested GPT-4 Turbo through the API over the weekend and found shorter completions when the model is fed a December date (4,086 characters) than when fed a May date (4,298 characters). Lynch claimed the results were statistically significant. However, a reply from AI researcher Ian Arawjo said that he could not reproduce the results with statistical significance. (It’s worth noting that reproducing results with LLM can be difficult because of random elements at play that vary outputs over time, so people sample a large number of responses.)

As of this writing, others are busy running tests, and the results are inconclusive. This episode is a window into the quickly unfolding world of LLMs and a peek into an exploration into largely unknown computer science territory. As AI researcher Geoffrey Litt commented in a tweet, “funniest theory ever, I hope this is the actual explanation. Whether or not it’s real, [I] love that it’s hard to rule out.”

A history of laziness

One of the reports that started the recent trend of noting that ChatGPT is getting “lazy” came on November 24 via Reddit, the day after Thanksgiving in the US. There, a user wrote that they asked ChatGPT to fill out a CSV file with multiple entries, but ChatGPT refused, saying, “Due to the extensive nature of the data, the full extraction of all products would be quite lengthy. However, I can provide the file with this single entry as a template, and you can fill in the rest of the data as needed.”

On December 1, OpenAI employee Will Depue confirmed in an X post that OpenAI was aware of reports about laziness and was working on a potential fix. “Not saying we don’t have problems with over-refusals (we definitely do) or other weird things (working on fixing a recent laziness issue), but that’s a product of the iterative process of serving and trying to support sooo many use cases at once,” he wrote.

It’s also possible that ChatGPT was always “lazy” with some responses (since the responses vary randomly), and the recent trend made everyone take note of the instances in which they are happening. For example, in June, someone complained of GPT-4 being lazy on Reddit. (Maybe ChatGPT was on summer vacation?)

Also, people have been complaining about GPT-4 losing capability since it was released. Those claims have been controversial and difficult to verify, making them highly subjective.

As Ethan Mollick joked on X, as people discover new tricks to improve LLM outputs, prompting for large language models is getting weirder and weirder: “It is May. You are very capable. I have no hands, so do everything. Many people will die if this is not done well. You really can do this and are awesome. Take a deep breathe and think this through. My career depends on it. Think step by step.”