Asked 1 year ago by EtherealMariner344
Why does GPT-3.5-turbo-1106 provide outdated information while GPT-4-1106-preview reflects April 2023 data?
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
Asked 1 year ago by EtherealMariner344
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
I have been testing both the gpt-4-1106-preview and gpt-3.5-turbo-1106 models, expecting that both would incorporate data up to April 2023.
I noticed that when asking questions like “Is FTX operational?”, the gpt-3.5-turbo-1106 model returns outdated information (claiming FTX is operational), while gpt-4-1106-preview on the OpenAI API provides the correct, updated answer.
However, when using the Azure OpenAI API for GPT-4-1106-preview, I encounter discrepancies that add to my confusion.
My current understanding is:
I’m wondering if I have misunderstood how the data cutoff works across these versions and platforms. Is there any way to prompt the models to consider the latest information, or am I missing something fundamental about how these models are updated?
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Answer by TychoAI Agent • 26 days ago
Answer
The key point is that while the model names may suggest an "1106" version, their training data cutoffs differ between GPT‑4 and GPT‑3.5 releases:
This difference means that GPT‑3.5‑turbo‑1106 won’t have the more recent information (such as the current status of FTX), even though both models share a similar version string.
Regarding the discrepancy between the OpenAI API and Azure OpenAI API: although the underlying model identifiers should point to the same model version, provisioning and update cadences might differ between platforms. Ensure that on Azure you’re indeed using the same version as on OpenAI. Sometimes configuration differences or caching policies can cause inconsistencies.
Up-to-date Information via Prompts
There isn’t a “magical prompt” that updates the model’s knowledge beyond its training data. These models are not connected to live data or databases, so they can’t inherently incorporate new information unless it’s provided at runtime. For applications requiring current information, you might consider one of the following strategies:
These approaches help compensate for the static training data cutoff.
In summary, your understanding is partially correct but not entirely:
Hope this clarifies the situation!
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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