Non-technical here, so be gentle! The company I work at is currently dipping our toes in the AI waters, to start building out some plans for how to embed AI into our enterprise systems for efficiency and ease of use. Some of this is straight foward, as system vendors add AI capabilities to existing systems. Other stuff is a bit more vexing. One possible use case would be to use an AI to answer questions on company policies, like “Am I eligible to take a paid day off for the death of a family member” or “Can I book business class on a trip from London to Tokyo”. In order to answer these questions, we’d have a database of various policies with tags on where, when and who those policies would apply to. An AI would then reference that info to provide answers to natural language queries. The concern is that you need the AI to not answer at all when the answer is not known. If an LLM comes to an edge case or grey area in a policy, I suspect it would produce a best-fit answer (hallucination), even if that answer isn’t actually in the database of policies. This could have significant ramifications, if, for example, it answers an HR policy question in a way that isn’t compliant with the relevant laws and regulations for that country/state/locality. So, what is the state of LLMs when it comes to being able to avoid hallucinations? Is there even a way to do this, given that everything to an LLM is just a guess, with higher or lower probability. How do you ensure an AI is sticking to policy and kicking grey areas out to a real human? submitted by /u/cousineye
Originally posted by u/cousineye on r/ArtificialInteligence
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