I want the user to be able to ask technical questions about a set of documents, then the user should retrieve a summary-answer from those documents along with a source.
I first need to finetune GPT4 so it better understands the niche-specific technical questions, the words used, etc. I could ask the finetuned model questions, but it won't really know from where it got the information. Without finetuning the summarised answer will suffer, or it will pull out the wrong papers.
Then I need to use a vector database to store the technical papers for the model to access; now I can ask questions, get a decent answer, and will have access to the sources.
Thanks (to both you and the parent) for sharing these details. So is it fair to say the following:
1. Fine-tuning bakes the knowledge into the model, but getting the "source" of an answer to a specific question becomes cagey and it is unclear if the answer is accurate or just a hallucination.
2. Therefore vector databases, which can provide context to the LLM before it answers, can solve this "citation" problem, BUT:
3. We then have limits because of the context window of the LLM to begin with.
Is that a fair understanding, or have I totally gotten this incorrect?
Edit: Or, are you saying that you both fine-tune AND also use a vector database which stores the embeddings of the dataset used to fine-tune the model?
I want the user to be able to ask technical questions about a set of documents, then the user should retrieve a summary-answer from those documents along with a source.
I first need to finetune GPT4 so it better understands the niche-specific technical questions, the words used, etc. I could ask the finetuned model questions, but it won't really know from where it got the information. Without finetuning the summarised answer will suffer, or it will pull out the wrong papers.
Then I need to use a vector database to store the technical papers for the model to access; now I can ask questions, get a decent answer, and will have access to the sources.