Hacker News new | past | comments | ask | show | jobs | submit login

eh, I think this is pretty reasonable and not a "hack". It matches what we do as people. I think there probably needs to be research into how to tell it when it doesn't know something, however.

I think if you remember that LLMs are not databases, but they do contain a super lossy-compressed version of (it's training) knowledge, this feels less like a hack. If you ask someone, "who won the World Cup in 2000?", they may say "I think it was X, but let me google it first". That person isn't screwed up, using tools isn't a failure.

If the context is a work setting, or somewhere that is data-centric, it totally makes sense to check it. Like a Chat Bot for a store, or company that is helping someone troubleshoot or research. Anything where it really obvious answers that are easy to learn from volumes of data ("what company makes the corolla?"), probably don't need fact checking as often, but why not have the system check its work?

Meanwhile, programming, writing prose, etc are not things you generally fact-check mid-way, and are things that can be "learned" well from statistical volume. Most programmers can get "pretty good" syntax on first try, and any dedicated syntax tool will get to basically 100%, and the same makes sense for an LLM.




This is similar to the difference between data dredging and scientific hypothesis testing.

'But what bias did we infer with [LLM knowledgebase] background research prior to formulating a hypothesis, and who measured?'

There are various methods of Inference: Inductive, Deductive, and Abductive

What are the limits of Null Hypothesis methods of scientific inquiry?


I think a better way would be to just use it as a text to search interface in the first place?




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: