On Tuesday, researchers from Stanford University and University of California, Berkeley released a research paper that purports to show changes in GPT-4’s outputs over time. The paper fuels a common-but-unproven belief that the AI language model has grown worse at coding and compositional tasks over the past few months. Some experts aren’t convinced by the results, but they say that the lack of certainty points to a larger problem with how OpenAI handles its model releases. In a study titled “How Is ChatGPT’s Behavior Changing over Time?” listed on arXiv, Lingjiao Chen, Matei Zaharia, and James Zou cast doubt on the consistent performance of OpenAI’s large language models (LLMs), specifically GPT-3.5 and GPT-4. Using API access, they tested the March and June 2023 versions of these models on tasks like math problem-solving, answering sensitive questions, code generation, and visual reasoning. Most notably, GPT-4’s ability to identify prime numbers reportedly plunged dramatically from an accuracy of 97.6 percent in March to just 2.4 percent in June. Strangely, GPT-3.5 showed improved performance in the same period.
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