Kaj Bostrom @alephic2
NLP geek with a PhD from @utcompsci, now @ AWS. I like generative modeling but not in an evil way I promise. Also at https://t.co/qxYqjaWU5W He/him bostromk.net New York, NY Joined December 2018-
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Kaj Bostrom retweeted
For inputs involving many steps, the operands for each step remain important until an identical depth. This indicates that the model is *not* breaking down the computation, solving subproblems, and composing their results together. 2/6
Definitely updated my mental model of CoT based on these results - give it a read, the paper delivers right off the bat and then keeps following up with more!
Definitely updated my mental model of CoT based on these results - give it a read, the paper delivers right off the bat and then keeps following up with more!
Kaj Bostrom retweeted
🍓 still has a way to go for solving murder mysteries. We ran o1 on our dataset MuSR (ICLR ’24). It doesn’t beat Claude-3.5 Sonnet with CoT. MuSR requires a lot of commonsense reasoning and less math/logic (where 🍓 shines) MuSR is still a challenge! More to come soon 😎
Kaj Bostrom retweeted
Super excited to bring ChatGPT Murder Mysteries to #ICLR2024 from our dataset MuSR as a spotlight presentation! A big shout-out goes to my coauthors, @xiye_nlp @alephic2 @swarat and @gregd_nlp See you all there 😀
Super excited to bring ChatGPT Murder Mysteries to #ICLR2024 from our dataset MuSR as a spotlight presentation! A big shout-out goes to my coauthors, @xiye_nlp @alephic2 @swarat and @gregd_nlp See you all there 😀
Kaj Bostrom retweeted
After extensive training with various music generation neural networks and dedicating countless hours to prompting them, it's become even more evident to me that relying solely on text prompts as interface for music creation significantly limits the creative process.
After extensive training with various music generation neural networks and dedicating countless hours to prompting them, it's become even more evident to me that relying solely on text prompts as interface for music creation significantly limits the creative process.
Kaj Bostrom retweeted
GPT-4 can write murder mysteries that it can’t solve. 🕵️ We use GPT-4 to build a dataset, MuSR, to test the limits of LLMs’ textual reasoning abilities (commonsense, ToM, & more) 📃 arxiv.org/abs/2310.16049 🌐 zayne-sprague.github.io/MuSR/ w/ @xiye_nlp @alephic2 @swarat @gregd_nlp
Kaj Bostrom retweeted
While demand for generative model training soars 📈, I think a new field is coalescing that’s focused on trying to make sense of generative models _once they’re already trained_: characterizing their behaviors, differences, and underlying mechanisms…so we wrote a paper about it!
Kaj Bostrom retweeted
LLMs are used for reasoning tasks in NL but lack explicit planning abilities. In arxiv.org/abs/2307.02472, we see if vector spaces can enable planning by choosing statements to combine to reach a conclusion. Joint w/ @alephic2 @swarat & @gregd_nlp NLRSE workshop at #ACL2023NLP
Kaj Bostrom retweeted
📣Call for papers! The Natural Language Reasoning and Structured Explanations Workshop will be the first of its kind at ACL 2023, and the deadline for paper submissions is April 24. Learn more and submit here: nl-reasoning-workshop.github.io
Kaj Bostrom retweeted
Three years in the making - our big review/position piece on the capabilities of large language models (LLMs) from the cognitive science perspective. Thread below! 1/ arxiv.org/abs/2301.06627
Luca Soldaini 🎀 @soldni
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