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?
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?
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?