this post was submitted on 04 Aug 2024
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The obvious easy solution would be to teach LLMs to guide the user through their "thinking process" or however you may call it. Instead of answering outright. This is what people do too, right? They look at what they thought and/or wrote. Or they would say "let's test this". Like good teachers do. Problem is, that would require some sort of intelligence, which artificial intelligence ironically doesn't possess.
There are chain of thought and tree of thought approaches and maybe even more. From what I understand it generates answer in several passes and even with smaller models you can get better results.
However it is funny how AI (LLMs) is heavily marketed as a thing that will make many jobs obsolete and/or will take over humanity. Yet to get any meaningful results people start to build whole pipelines around LLMs, probably even using several models for different tasks. I also read a little about retrieval augmented generation (RAG) and apparently it has a lot of caveats in terms of what data can and can not be successfully extracted, data should be chunked to fit into the context and yet retain all the valuable information and this problem does not have "one size fits all" solution.
Overall it feels like someone made black box (LLM), someone tried to use this black box to deal with the existing complexity, failed and started building another layer of complexity around the black box. So ultimately current AI adopters can find themselves with two complex entities at hand. And I find it kind of funny.