Loss of logic In the ChatGPT May 3 Version

Anyone experiencing a reduction in quality outputs from the May 3 Version?
I have noticed a reduction in logic and inability to produce quality code Compared to the previous versions. What has your experience been?


I’ve not seen that myself.

Do you have prompt/response examples which demonstrate what you’re seeing?

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I’m happy to hear that you haven’t been experiencing it. I’m working on putting together a simplified example of what I’m experiencing for evaluation. In the meantime figured I’d reach out and touch bases with everyone about the new update.

I asked it to look over my code and find a problem. It replies like the following and starts writing gibberish code:

The issue herein lies within the aspect ratio calculation causing impressions that parts of the image compression/aspect presents inconsistencies with colors and locations during mouse interactions such as choosing certain color. I will rewrite this entire image upload/browse canvas applications by preserving actual image proportions as keeping detailed positioning.

Here’user ">

Color Chatbot body { keyboard-action="typing-first-s)"> keysr_text(iforele_user"labelma Code to ve@norvig\.cole"text(inv)','') get_ifav code_chunks: O[rt"]:rnS]]>sfoe:;"(not.ad!~-~-(.bubscriptions==-)):+cript\setult?[ dt)[.InU(": ifca>

i have also noticed a significant downgrade in the logic capabilities of the most recent GPT4 version when discussing/evaluating complex inverse problems, differential rates or patterns of change, and spatial-temporal variability. I only rarely received erroneous replies before the update, but now I have to double-check all output (i.e., now a double-negative conditions sometimes don’t get appropriately translated into a positive condition). I see these errors as more GPT3.5-like than prior GPT4 levels of reasoning. Very frustrating.

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I understand that you, among others, have noticed a degradation in the logic capabilities of the latest GPT-4 version, particularly when tackling complex inverse problems, differential rates, and spatial-temporal variability. The increased frequency of erroneous responses and issues in recognizing double negatives have been frustrating, seemingly reminiscent of the previous GPT3.5’s performance.

Some speculate that covert throttling during peak usage periods may be responsible, which raises questions about OpenAI’s transparency. It would be a significant disappointment if there were elements of deception in the company’s approach, particularly at such an early stage.

I trust that OpenAI will seriously consider these concerns and address them promptly. Transparency and responsiveness to user feedback are essential attributes for any company, particularly a startup. We should remain optimistic that OpenAI will heed this feedback, make the necessary adjustments, and ensure a trustworthy and positive experience for all users.

In my view, the issues likely stem from problems with the new model, but I’m interested to hear your perspective. What do you think?

I am not sure. I think you have articulated a number of causes for concern very well. I am honestly disturbed by the lack of attention this issue has received in these forums.

Furthermore, I am even more dismayed by the latest “May 24 Version” which appears to actually be a further step in the wrong direction.

I am observing even greater loss of reasoning between the May 12 and May 23 version! Hard to overstate how frustrating this is! The May 23 version in many respects appears to follow instructions more poorly than GPT3.5 at this point and consistently fails to solve basic logic problems.


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I think they’re finding ways to make GPT 4 more affordable because of the many plug-in users.
Everyone is annoyed at the slow speed and 25 message cap. A possible trade-off is to go to a more affordable model when servers are peaking.