Ben Hyman (@revisenretweet) 's Twitter Profile
Ben Hyman

@revisenretweet

Economist @NewYorkFed, currently visiting UC Berkeley. @Wharton PhD. #EconTwitter: PF, labor, urban/spatial. ⚽️: @OL, @ChelseaFC. Views here are my own.

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linkhttps://benhyman.com calendar_today10-01-2014 02:43:02

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Ethan Mollick (@emollick) 's Twitter Profile Photo

Paper from OpenAI says hallucinations are less a problem with LLMs themselves & more an issue with training on tests that only reward right answers. That encourages guessing rather than saying “I don’t know” If this is true, there is a straightforward path for more reliable AI.

Paper from OpenAI says hallucinations are less a problem with LLMs themselves & more an issue with training on tests that only reward right answers. That encourages guessing rather than saying “I don’t know”

If this is true, there is a straightforward path for more reliable AI.
Ben Hyman (@revisenretweet) 's Twitter Profile Photo

We have now made public our AI Retrainability (AIR) index values and occupation rankings by both 2-digit and 6-digit SOC. Public link here: tinyurl.com/mvuku2td

Ben Hyman (@revisenretweet) 's Twitter Profile Photo

I agree! There is also potentially greater scope for policy ideas like AI insurance (Ioana Marinescu) or Automation Adjustment Assistance (sam manning ) to work relative to TAA. While trade shocks are spatially concentrated, AI shocks are likely disperse --> fewer adjustment frictions.

Paul Novosad (@paulnovosad) 's Twitter Profile Photo

What happens when online job applicants start using LLMs? It ain't good. 1. Pre-LLM, cover letter quality predicts your work quality, and a good cover gets you a job 2. LLMs wipe out the signal, and employer demand falls 3. Model suggests high ability workers lose the most 1/n

What happens when online job applicants start using LLMs? It ain't good.

1. Pre-LLM, cover letter quality predicts your work quality, and a good cover gets you a job
2. LLMs wipe out the signal, and employer demand falls
3. Model suggests high ability workers lose the most

1/n