I am baffled by the fact that when I ask ChatGPT to look up some stock data (for example) it spits out errors. How does it not have a built in ability to check its own output?
I can easily ask an LLM to write be a function in a random programming language, then feed the output to a compiler, and pipe errors from the compiler back to the LLM.
What doesn't work so well is typing "pong in java" into a bash shell.
This isn't a perfect solution (not even for small projects), but it does demonstrate that automated validation can improve the output.
If you're completing the sentence "What is the current price of Apple?..." based on the internet as a training source, the most likely reply is not:
"Well, let's think about how we'd go about this, I should do a search for AAPL, and then..."
The most likely completion is: "The price of AAPL is $X"
OpenAI had to "artificially" bias it to say "I can't answer that" or it'd happily tell you a million things like that.
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On the other hand, give the LLM room to plan, it will plan. If you ask it "To answer x what do you need", it does better at answering x
The most likely completion to "How would you tell me Apple's current price", is a logical step by step process. And that's how it gets a chance to check itself.
I think people underestimate how much logic the model can do in a single pass, LLMs need to output things that we assume can be done with working memory
won't be one size fits all due to various types of data - and if you could validate all types - why do you need the llm!