🔗 My fine-tuned Model failed, and Here's Why
Since fine-tuning was announced by OpenAI, it has been a very prominent topic in the AI community. Many members have explored different approaches, applied them to their use cases, and shared their results.
Most of those articles and posts are focused on comparing fine-tuned GPT-3.5 with GPT-4.
"But... how about few-shot GPT-3.5 vs fine-tuned GPT-3.5?"
OpenAI's GPT models are exceptionally good at following instructions based on examples, which is known as "few-shot learning".
When it comes to handling more complex chains that require multiple instructions and specific output formats, I have found that few-shot GPT-3.5 Turbo performs incredibly well, while still being affordable and fast.
For that reason, this approach is my go-to, and I am using it for most of my LLM endpoints in production.
That being said, I have not tried fine-tuning yet, and the premise of using fewer tokens, faster response times, and still getting a nicely formatted result seems very promising.
The fundamental question that I want to explore in this article is:
"Can fine-tuned GPT-3.5 turbo outperform few-shot GPT-3.5 Turbo?"
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