4 papers
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1
Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable. Yet, many real-world r…
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Liyi Zhang, Michael Y. Li, R. Thomas McCoy +3
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture as…
MacGyver: Are Large Language Models Creative Problem Solvers?
Yufei Tian, Abhilasha Ravichander, Lianhui Qin +6
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting o…
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity
Raja Marjieh, Sreejan Kumar, Declan Campbell +4
Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has…