Ask HN: How do you make LLM generated text believable?
As a graudate student who just working as management & IR, I use LLM to do daily jobs , including weekly briefing and
But AI generated report looks too good to be checked, and hallucination can't be terminated. But Boss and SEC can not tolerate any number mistakes.
So, can you tell me, how do you solve this problem?
10 comments
I am sorry that I did not explain my question explicitly. I write recurring weekly briefings for internal use, such as market insights and industry news.
I’m not trying to make AI-generated text “believable”. I’m asking almost the opposite question: when an AI generated text is fluent enough to hide mistakes, how do human check how to systematically check numbers, dates, cites and judgements?
Have it output the numbers it's basing the conclusion on, have it output a program that it's used to do math to derive judgements.
Yes, i agree that quantitative part， like dates, numbers, amounts should be extracted and let the LLM to output original numbers and computation steps. That's not hard for a briefing harness.
However, my most confusion part is qualitative side, like market insight from a news,policy change and interpretation, and industry NEWS interpretation, they are not straight math but they need tracebility. Do you have any idea to solve those judgement claim?
If you were writing it yourself you would proofread and double-check everything. Why do you think that’s no longer required?
> But AI generated report looks too good to be checked
have you considered...checking it anyway, no matter how good it looks?
have you considered...checking it anyway, no matter how good it looks?
I don't mean never check it by myself, but I want to discuss about a methodology to ensure AI generated text has the same auditability and tracebility like human wrote text.
> So, can you tell me, how do you solve this problem?
Write it yourself.
Write it yourself.