Stanford and UC Berkeley researchers recently conducted a comprehensive study to analyze the performance and improvement of OpenAI’s ChatGPT models, specifically GPT-3.5 and GPT-4. The study aimed to assess their capabilities in various tasks such as math problem-solving, answering sensitive questions, code generation, and visual reasoning.
Surprisingly, the results revealed a significant decrease in GPT-4’s performance across multiple areas from March to June. In terms of math problem-solving, the once highly accurate GPT-4 experienced a drastic drop, plummeting from an impressive 97.6% accuracy to a mere 2.4%. On the other hand, GPT-3.5 showcased improvement and managed to produce correct answers during the same timeframe.
Furthermore, GPT-4’s abilities in generating executable code also saw a decline. The percentage of directly executable code generations fell dramatically from 52% to just 10%. This decline raises concerns about the model’s reliability and efficiency for coding tasks.
One of the most striking findings from the study pertained to GPT-4’s responses to sensitive questions. The model exhibited a significant decrease, with its answer rate dropping to a mere 5% in June compared to 21% in May. This decline raises questions about the model’s accuracy and responsiveness, particularly when dealing with sensitive and nuanced topics.
In contrast, GPT-3.5 demonstrated a slight increase in the number of questions answered correctly in June compared to the previous month. This finding indicates that GPT-3.5 could potentially be a more reliable choice for users seeking accurate and consistent responses.
The researchers emphasized the importance of regularly evaluating the models’ abilities due to the fluctuations in their performance. Users relying on GPT-3.5 and GPT-4 should remain vigilant and consider assessing the models’ performance in real-time to ensure optimal results.
The study’s findings also prompt users to reevaluate their reliance on GPT-4, raising concerns about its decline in quality and the specifics of its training process. These results encourage users to explore alternative models or solutions that align better with their needs and expectations.
In conclusion, the study conducted by Stanford University and UC Berkeley sheds light on the performance discrepancies between GPT-3.5 and GPT-4. The findings highlight the need for users to continually evaluate the models’ abilities and consider alternatives based on their specific requirements.
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