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Comparing the Decline in ChatGPT-4 Performance with the Advancements of ChatGPT-3.5: A Comparative Study of AI Language Models

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Comparing the Decline in ChatGPT-4 Performance with the Advancements of ChatGPT-3.5: A Comparative Study of AI Language Models

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Users of OpenAI’s artificial intelligent chatbot in the form of ChatGPT-4 vs ChatGPT-3.5 might have noticed changes since their introduction. Now researchers from Stanford University and UC Berkeley, have benchmarked the GPT-4 performance in March 2023, and the model’s performance in June 2023 providing insight into changes in the AI’s performance.

As artificial intelligence (AI) expands its horizons, the influence of Large Language Models (LLMs), such as GPT-3.5 and GPT-4, is becoming increasingly dominant. While these computational giants have redefined the boundaries of AI, the way they evolve over time, however, can be a puzzle to users and developers alike.

Changes in ChatGPT performance

Often, minor updates in LLMs can trigger significant performance variations. Researchers from Stanford University and UC Berkeley conducted a comparative study of GPT-3.5 and GPT-4 versions from March 2023 and June 2023. They scrutinized their performance in four diverse tasks: mathematics problem-solving, handling sensitive queries, generating code, and visual reasoning. The results were fascinating: even in a short span of time, the same LLM’s performance can transform dramatically.

The updates introduced to LLMs are supposed to improve their functionality, but the reality is more complicated. For instance, GPT-4’s aptitude to recognize prime numbers plunged from an impressive 97.6% accuracy in March 2023 to a mere 2.4% in June 2023. Conversely, GPT-3.5 significantly ameliorated its performance in the same task over this period. Thus, the impact of updates on these models is far from predictable, underscoring the need for vigilant monitoring.

LLM updates

The uncertain nature of LLM updates poses a significant challenge to their integration into larger workflows. A sudden change in an LLM’s response to a prompt can derail the downstream pipeline and complicate the reproduction of results. Navigating this unpredictability is a considerable challenge for developers and users alike.

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This study underscores the vital need for persistent monitoring of LLM quality. As updates aiming to enhance certain aspects of the model might inadvertently impact its performance elsewhere, it’s crucial to stay updated on these models’ capabilities.

ChatGPT-4 vs ChatGPT-3.5

Current research doesn’t adequately monitor the longitudinal drifts of widely used LLM services like GPT-4 and GPT-3.5 over time. This monitoring of performance shifts is emerging as a vital aspect of deploying machine learning services in a rapidly evolving technological landscape.

The performance of LLMs can vary significantly across different tasks. For example, in June 2023, GPT-4 was more reluctant to respond to sensitive queries than it was in March, and both GPT-4 and GPT-3.5 showed an increased number of formatting errors in code generation.

The behavior of LLMs like GPT-3.5 and GPT-4 can alter significantly within a short span of time. As these models continue to evolve, understanding their performance across different tasks and gauging the impact of updates on their capabilities becomes all the more crucial. The need of the hour is continuous monitoring and evaluation of these models to ensure their stability and reliability. Read the full paper on the arXiv website for all the details and testing carried out in the ChatGPT-4 vs ChatGPT-3.5 showdown.

Source : TPU : arXiv

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