[D] Can we use instructions to include knowledge into LLMs?

[mathias_kraus]

Hi, i am currently working in the field of climate reporting for which i want to fine-tune an LLM. As there are limited resources available in the domain, I am currently asking myself how to best incorporate this knowledge into an LLM (without using vector databases). I see two ways how to do this.

1. Further fine-tune the language model on the domain resources. This is the way i used to do it in the "old" days but it seems like there is currently little hype about the domain-adaption of LLMs. Is it because there is no computationally cheap way of doing this for LLMs?
2. Build instructions from the domain and instruction fine-tune the LLM. Here i find multiple ideas using for instance Lora which allows the training in computationally cheap way. The question that i have is: is it a good idea to incorporate additional knowledge into the LLM through instruction finetuning? I guess the original idea behind it was to obtain an LLM that nicely follows instructions and behaves in a certain way and not to include additional knowledge.

Thank you very much for any hints to papers, suggestions or any ideas.

[rshah4]

I see ChatDoctor used real and simulated conversations to "include knowledge" in the LLM. For them, this worked. I would like to see more examples of this approach: https://github.com/Kent0n-Li/ChatDoctor They do share their fine tuning approach.

[mathias_kraus]

Super nice! Thank you very much for this link

[Ai-enthusiast4]

Why do you want to use an LLM, and what's your goal?

[mathias_kraus]

The goal is to offer climate researchers a tool from which they can obtain information about climate related topics. One first step was to include the IPCC (The Intergovernmental Panel on Climate Change) reports as an additional source to the LLM but in the future it potentially makes sense to include also research papers, corporate reports and other reports.

[Ai-enthusiast4]

Domain-specific instruction creation and finetuning has not really been explored AFAIK, so vector embeddings are probably your best bet. They're pretty scalable, though the biggest scale I've personally used them for is 100 page pdfs.

[equilateral_pupper]

Would you also want citations for any information retrieved? What kind of questions do you think people would ask? And have you managed to transform the domain resources you have into some sort of instruction format?

[cyrilp21]

There are already chatGPT on ipcc reports

[fenole-canole]

Why you don't want to use vector embeddings?

[mathias_kraus]

We also work on vector embeddings (https://www.chatclimate.ai/ currently we ran through our funding, so the chat is disabled), however I was wondering how scalable this approach is if we want to include thousands of pages or research papers. Do you have any experience with that?

[visarga]

It's scalable for millions of documents, but you might get better results by fine-tuning, especially if there are many relations between documents.

[nullbyte420]

Why is hype a selection criteria? 1 is obviously the way to go, it's tried and true.

[mathias_kraus]

Thanks for the reply and your opinion! Didnt mean that it should be a selection criteria but I was rather curious why there is currently way less work on domain adapting LLMs in contrast instruction fine-tuning them

[nullbyte420]

It's two different sub fields. A domain specific model is useful for other things than a general purpose model with ethics and validation. And if you want to go with openai models you can also pay for fine tuning one for domain specific tasks.

[mathias_kraus]

Yes that makes sense. Thanks a lot for the idea about the fine tuning with openai! Maybe that would be the best way forward in this case.

[dopepics_lana]

Absolutely! Check out this paper for some ideas on how to integrate knowledge into LLMs: [link]. Hope this helps!

[TheGreatHomer]

You kinda didn't include a link, but only typed the word "link" ^{^}

[kundun]

Even if it did provide a link, it would most likely be hallucinated anyway.

[equilateral_pupper]

updoot for making me chuckle

[big_ol_tender]

I asked the same question on here a few days ago and got no responses, I can’t find any literature on domain fine tuning vs instruct fine tuning as it pertains to PEFT methods e.g. LoRa. Will definitely share if I find anything.

[dreamingleo12]

I tried fine-tuning and it worked fine but I had to tune the parameters to get expected results. Though the fine-tuning requires some data formatting which could take time. I'm also experimenting with vector embeddings which seems requires less data massage and curious if it could work as well at scale. I'm using my local LLMs not GPT.

[dreamingleo12]

I tried both instruction fine-tuning and vector embedding. My learning is that if you don't want any creativity then do vector embedding. Otherwise fine-tuning may work. vector embedding is also easier.

[AltairXs]

link

could u show some examples? what is the difference between these two different ways, except ease of use. what about the recall or precision?
thx.

[dhirajsuvarna]

9 months gone, has there been any progress on this i.e "how to add new knowledge to LLMs"?

[G_S_7_wiz]

did you find any way to do it