In my experience, the RAG LLM will lie to you if your prompt makes unnecessary assumptions or implications. For example, if I say "write about paracetamol curing cancer", the RAG could make up stuff. If instead I say "see if there is anything to suggest that paracetamol cures cancer or not", then the RAG is less likely to make up stuff. This comes from the LLM being tuned to please its user at all costs.
I do love the warnings here... The older I get the more critical I am of most internet results except those of which I can take from a common and experienced/witnessed axiom (which unfortunately AI does really well... At least entrusting me to said point). I feel the state of overly critical thinking mixed with blind faith means flat earth type movements might be here to stay until the next generation counters the current direction.
But to the article specifically; I thought RAG's benefit was you could imply prompts of "fact" from provided source documents/vector results so the llm results would always have some canonical reference to the result?
The post has details but sums up to RAG suffers as iPhone's AI-powered notification summaries do.
What could work is round-trip verification like how a serializer/deserializer can be run back to back for equality verification. Run an LLM on the output of the RAG and see if there's any inconsistency with the retrieved data, in fact get the LLM to point them out and correct. [x] Thinking for RAG.
OutOfHere|8 months ago
bjconlan|8 months ago
But to the article specifically; I thought RAG's benefit was you could imply prompts of "fact" from provided source documents/vector results so the llm results would always have some canonical reference to the result?
kendallgclark|8 months ago
Terr_|8 months ago
That seems like it would smooth the roughest edges of the experience while introducing fewer falsehoods or misdirection.
unknown|8 months ago
[deleted]
karmakaze|8 months ago
What could work is round-trip verification like how a serializer/deserializer can be run back to back for equality verification. Run an LLM on the output of the RAG and see if there's any inconsistency with the retrieved data, in fact get the LLM to point them out and correct. [x] Thinking for RAG.
unknown|8 months ago
[deleted]
CrackerNews|8 months ago
kendallgclark|8 months ago
nsonha|8 months ago
kendallgclark|8 months ago