Can fine tuning replace the retrieval step i.e. is it possible to fine tune the model so it knows all the knowledge from my organization and we skip the retrieval step during a chat about the data
A problem with fine tuning based on organization data is that if the underlying data changes, you'd need to fine-tune the model again each change. This might be okay for one-off changes (such as the name of the model in the example) but if it costs $300 each time (not to mention the time spent) and you have 100s/1,000s of changes per month, it's not really viable.
The finetuning can tailor the model to have more customized knowledge, just like the identity knowledge of itself shown in the blog post. If you ask the original llama model, it should know nothing about SkyPilot or Vicuña, as it is trained on old knowledge from the internet.
However, finetuning still cannot get rid of the hallucination problem that all the chatbot suffers from.
It depends on how accurate you expect the chatbot should be. The retrieval might be considered more accurate, as it will not make up solutions, but just return irrelevant answer in the worst case.
You could finetune then add on a retrieval step, which has the advantage of citing sources. Jury's probably still out on which, or which combination of, methods work best. Likely use case and data size-dependent.
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Can fine tuning replace the retrieval step i.e. is it possible to fine tune the model so it knows all the knowledge from my organization and we skip the retrieval step during a chat about the data
A problem with fine tuning based on organization data is that if the underlying data changes, you'd need to fine-tune the model again each change. This might be okay for one-off changes (such as the name of the model in the example) but if it costs $300 each time (not to mention the time spent) and you have 100s/1,000s of changes per month, it's not really viable.
The finetuning can tailor the model to have more customized knowledge, just like the identity knowledge of itself shown in the blog post. If you ask the original llama model, it should know nothing about SkyPilot or Vicuña, as it is trained on old knowledge from the internet.
However, finetuning still cannot get rid of the hallucination problem that all the chatbot suffers from. It depends on how accurate you expect the chatbot should be. The retrieval might be considered more accurate, as it will not make up solutions, but just return irrelevant answer in the worst case.
You could finetune then add on a retrieval step, which has the advantage of citing sources. Jury's probably still out on which, or which combination of, methods work best. Likely use case and data size-dependent.