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Sure, chains of networks can guess at the "passable" answer much better/faster/cheaper etc. But that doesn't remove the core issue, that none of the sub-network
by Yizahi 2y ago
Sure, chains of networks can guess at the "passable" answer much better/faster/cheaper etc. But that doesn't remove the core issue, that none of the sub-networks or decision trees can understand what it generates, and so it can't abort its work and output "no answer" or something similar.
The whole premise of original request was that user raises a task for NN which has a verifiable (maybe partially) answer. He sees incorrect answer and wishes that a "failure" was displayed instead. But NN can't verify correctness of it's output. After all G in GPT stands for Generative.
- berkes 2y agoMy simple RAG setup has a steps that will return "We don't have this information" if e.g. our vector DB returns entries with far too low relevancy scores or if the response from the LLM fails to add certain attributes in its answer and so on. Edit: TBC: these "steps" aren't LLMS or other models. They're simple code with simple if/elses and an accidental regex. Again: an LLM/NN indeed has no "understanding" of what it creates. Especially the LLMs that are "just" statistical models. But the tooling around it, the entire chain can very well handle this.
- robwwilliams 2y agoPerfect. We need that society of modules.