David Wingate (@davidwingate) header
David Wingate (@davidwingate) profile picture

Father of nine. Professor of computer science at BYU; working on big language models. Mormon and lovin' it!

Provo, UT
Joined June 2010
Photo by David Wingate (@davidwingate)Photo by David Wingate (@davidwingate)Photo by David Wingate (@davidwingate)Photo by David Wingate (@davidwingate)
David Wingate retweeted
1/ Can AI chat assistants improve conversations about divisive topics such as gun control? I was very fortunate to join a team of outstanding researchers who designed an experiment intended to answer this question: arxiv.org/abs/2302.07268
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David Wingate retweeted
This is today's #mustread and easily one of the top #ai papers of 2022 on first glance. I want to dig more into the methods on this, but my preliminary run through finds some fascinating patterns! @sheabrownethics , would you want to join F. LeRon Shults…lnkd.in/e2QR6SbW
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It's so cool to see this work go from a random idea @ChrisRytting and team had to a significant and impactful paper. You all should read the paper and follow Chris.
For Import AI, I wrote about a very special paper which I think has some significant implications. If we can use LLMs as proxies for people (for a certain level of detail and desired response accuracy), then I expect a bunch of strange things to happen. jack-clark.net/2022/10/11/im…
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David Wingate retweeted
LLMs may not 'understand' people, but they are incredibly good at approximating people (and things that people do). The thing I find consistently confusing is figuring out where approximation ends and understanding begins. I myself feel most of my insights are from approximation
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David Wingate retweeted
Using LLMs for social (simulation) science arxiv.org/abs/2209.06899 Once we figure out how to scale it, this approach could revolutionize agent-based simulation modeling for nuanced, multifaceted silicon samples cc @mjbommar,@computational,@jg_environ h/t @jackclarkSF Import AI
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David Wingate retweeted
For Import AI, I wrote about a very special paper which I think has some significant implications. If we can use LLMs as proxies for people (for a certain level of detail and desired response accuracy), then I expect a bunch of strange things to happen. jack-clark.net/2022/10/11/im…
Using LLMs for social (simulation) science arxiv.org/abs/2209.06899 Once we figure out how to scale it, this approach could revolutionize agent-based simulation modeling for nuanced, multifaceted silicon samples cc @mjbommar,@computational,@jg_environ h/t @jackclarkSF Import AI
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David Wingate retweeted
Thanks for the interest in our work! We're working on figuring out how prompting GPT-3 compares to priming experiments with humans, but we don't have a systematic answer yet. Stay tuned! @NancyFulda @joshua_gubler @EthanBusby @davidwingate @ChrisRytting
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David Wingate retweeted
What would a social science Turing test look like? Amazing new research by @lpargyle et al. shows GPT-3 can create “silicon samples” that impersonate respondents to large surveys such as the American National Election with remarkable accuracy. tinyurl.com/yawzftfz.
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David Wingate retweeted
Prompts are the bread and butter of LLMs. ✨But can they be compressed?✨ In new work at Findings of EMNLP, we show that prompts can be compressed ⬇️ while maintaining the most crucial information! ℹ️
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David Wingate retweeted
This work was done in collaboration with @davidwingate and @MohammadShoeybi near the end of my time @byu - reach out with questions!
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David Wingate retweeted
Exciting project, co-authored with @davidwingate @ChrisRytting @lpargyle @NancyFulda . Although we didn't ask about Sesame Street (we should have!)
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In a new paper, we ask whether you can use GPT-3 to survey humans by simulating those humans and asking them questions, as opposed to interviewing the actual humans. arxiv.org/abs/2209.06899 w/ @davidwingate @EthanBusby @joshua_gubler @lpargyle @NancyFulda
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Can we use GPT-3 to better understand human preferences? It was trained on the internet after all. Really cool work from @lpargyle, @davidwingate, @ChrisRytting, and team arxiv.org/abs/2209.06899
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Excited and grateful that our research team just received an NSF EAGER grant to further research using large-scale language models to study human attitudes. A great team: @lpargyle @EthanBusby @davidwingate @NancyFulda @ChrisRytting @tsor13
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9 kids at vocal point! Better than TV. #byuvocalpoint
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