João Loula
@joaoloula.bsky.social
13 followers 22 following 4 posts
PhD student @probcompproj and @MITCoCoSci, working on scaling data science using probabilistic programming.
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Reposted by João Loula
benlipkin.bsky.social
Many LM applications may be formulated as text generation conditional on some (Boolean) constraint.

Generate a…
- Python program that passes a test suite.
- PDDL plan that satisfies a goal.
- CoT trajectory that yields a positive reward.
The list goes on…

How can we efficiently satisfy these? 🧵👇
joaoloula.bsky.social
Correction: poster number is 634 :)
joaoloula.bsky.social
- Cast controlled generation as an inference problem, with the LM as a prior and verifiers and scorers as likelihood
- Use Sequential Monte Carlo to sample from the resulting posterior

Library w/ tutorials for setting up your own controlled generation inference problems: github.com/genlm/genlm-...
GitHub - genlm/genlm-control: Controlled text generation with programmable constraints.
Controlled text generation with programmable constraints. - genlm/genlm-control
github.com
joaoloula.bsky.social
#ICLR2025 Oral

How can we control LMs using diverse signals such as static analyses, test cases, and simulations?

In our paper “Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo” (w/ @benlipkin.bsky.social,
@alexlew.bsky.social, @xtimv.bsky.social) we: