RAFT is amazing!!
If you are using LLM RAG – you should be doing RAFT
11–15 of 15 posts
Re: If you are using LLM RAG – you should be doing RAFT
#12[deleted]
Re: If you are using LLM RAG – you should be doing RAFT
#13From [1]:
> We demonstrate that our RAG approach trains the model to perform better RAG on the set of documents it is trained on i.e., in-domain. By removing the oracle documents in some instances of the training data, we are compelling the model to memorize domain-knowledge.
What if you wanted to train it to say that it didn't find the answer?
Re: If you are using LLM RAG – you should be doing RAFT
#14Page me when this process is at least partially automated and continuous.
Call me when there’s a nix flake for it!
Re: If you are using LLM RAG – you should be doing RAFT
#15> Retrieval Aware Fine-Tuning (RAFT), presents a novel recipe to prepare fine-tuning data to tailor the models for domain-specific open-book setting, equivalent to in-domain RAG.
I cannot see the insight on why this is a for a limited domain? The key problem that is being solved is the known problem where RAG returns an irrelevant chunk. It seems like the "benefit" is training a model to ignore irrelevant chunks.
I am guessing because it costs money to train on multi-domains so they limited their research on one-domain at a time but not sure if there is a "bigger reason" why this isn't an approach to a fine-tuned "make answers from only relevant chunks" model? The paper seems to imply this is only works for specific-domains but I can't see why.