Hacker News Reader

Comment by danielmarkbruce | Hacker News Reader

>you have to have training data with accurate probabilities

This was your claim. If you can't read and understand that paper in relation to your claim, you are out of your depth. You haven't made a single claim relevant to that paper - just hand wavy comments about data.

View on HN

I clarified multiple times that I meant "accurate, representative data" to "derive accurate probabilities"

you are still employing underhanded techniques in an attempt "win an internet debate" (my impression)

try being more accommodating and flexible over repeating the same lame things

it's not hard to say, "ah I see what you were trying to say..." and move towards a more constructive conversation

RLCR / Jev et al. can only give as accurate predictions and probabilities as the underlying data they are trained on represents. Biased data results in biased probabilities, no algorithm fixes this. Can we agree on this point?

They post train on big math and improve calibration across five different non math benchmarks so the new claim "can only give as accurate predictions and probabilities as the underlying data they are trained on represents" is also off base. RLCR generates its own calibration examples from ordinary questions and answer keys. RL usually isn't trying to represent a data set, it's closer to search.

I'm not expecting you to human RL here, but I do hope next time you remember there is another imperfect human behind the screen and are less too online in future interactions

https://www.youtube.com/watch?v=c1Fv1uKTd-w

oh-seven

Reply on news.ycombinator.com