3 ms·
What's fascinating for me is how directly LangChain's Input > Thought > Action > Observation maps to the Observe > Orient > Decide > Act (OODA) Loop that is use
by akhayam 3y ago
What's fascinating for me is how directly LangChain's Input > Thought > Action > Observation maps to the Observe > Orient > Decide > Act (OODA) Loop that is used in many other industries: https://en.wikipedia.org/wiki/OODA_loop https://en.wikipedia.org/wiki/OODA_loop. The fact that we can do OODA loops without a human in the loop--by just passing a bunch of tools and adding memory to a LangChain--is simply mind blowing.
Having said that, I believe LangChain can benefit from incorporating learnings/failures from successful OODA Loops where human feedback continually improves the automation. I am not an expert, but it seems like this type of human feedback can be incorporated into LangChain by plugging in an RLHF framework. Not sure though if this RLHF framework plugs into Thought or Action.
- akiselev 3y agoA lot of those human lessons learned and failures probably are incorporated in the training data. I wonder if literally telling it to perform an OODA loop in those words might allow it to perform better than otherwise, using reasoning examples from the literature.
- akhayam 3y agoWell, that's something that I want to understand better. I know ChatGPT--inspired by InstructGPT--used RLHF to improve their models. Since OpenAI's model is not available, how does LangChain make sure it gets similar performance as ChatGPT without using RLHF underneath? The only other way I can think of is for them to take a core dependency on OpenAI's API which seems like a bad strategy for a promising open-source project like LangChain. How do you see this evolving?