140 citations · 411 across the 24 of their papers we have counts for
18 papers · 1 filter
Hypothesis Search: Inductive Reasoning with Language Models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia +3
Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which robustly generalize to novel scenarios. Recent work eva…
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabi…
Active Learning Helps Pretrained Models Learn the Intended Task
Alex Tamkin, Dat Nguyen, Salil Deshpande +2
Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data. An example is an object class…
STaR: Bootstrapping Reasoning With Reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu +1
Generating step-by-step "chain-of-thought" rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, in…
Temperature as Uncertainty in Contrastive Learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti +1
Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…
Improving Compositionality of Neural Networks by Decoding Representations to Inputs
Mike Wu, Noah Goodman, Stefano Ermon
In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs toget…