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About this paper
Jacob Andreas, Dan Klein, Sergey Levine
The paper proposes Learning with Latent Language (L3), a meta-learning approach that uses natural language as a latent parameter space to represent tasks. This method improves few-shot learning generalization and efficiency across visual reasoning, string editing, and reinforcement learning domains.
Key insights
- Natural language provides a rich source of information about human abstractions, offering a powerful and generalizable inductive bias for machine learning systems.
- Operating in a discrete, compositional language space enables models to better capture and generalize to novel concepts, improving sample efficiency.
- Language can serve as an effective internal, latent hypothesis space for tasks, even when no explicit language data is available during concept learning.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.