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RAGs do not prevent hallucinations nor does it guarantee that the quality of your output is contingent solely on the quality of your input. Using LLMs for legal
by threeseed 2y ago
RAGs do not prevent hallucinations nor does it guarantee that the quality of your output is contingent solely on the quality of your input. Using LLMs for legal use cases for example has shown it to be poor for anything other than initial research as it is accurate at best 65%:
https://dho.stanford.edu/wp-content/uploads/Legal_RAG_Hallucinations.pdf https://dho.stanford.edu/wp-content/uploads/Legal_RAG_Halluc...
So would strongly disagree that LLMs have become “good enough” for real-world applications" based on what was promised.
- mattyyeung 2y agoYou may be interested "Deterministic Quoting"[1]. This doesn't completely "solve" hallucinations, but I would argue that we do get "good enough" in several applications Disclosure: author on [1] [1] https://mattyyeung.github.io/deterministic-quoting https://mattyyeung.github.io/deterministic-quoting
- threeseed 2y agoHave seen this approach before. It's the yes we hallucinate but don't worry because we provide the sources for users to check. Even though everyone knows that users will never check unless the hallucination is egregious. It's such a disingenuous way of handling this.
- phillipcarter 2y ago> So would strongly disagree that LLMs have become “good enough” for real-world applications" based on what was promised. I can't speak for "what was promised" by anyone, but LLMs have been good enough to live in production as a core feature in my product since early last year, and have only gotten better.