2026-10-06 · America/Los_Angeles · 社区动态 · #10

Learning to Learn a Language: in-context learning of natural language from a synthetic non-linguistic prior [R]

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the idea behind TabPFN) showed that a model trained only on synthetic data can learn from real tabular data entirely in context. I wanted to share our paper "Learning to Learn a Language" where we extend the idea to structured sequences such as natural language. We propose a prior over languages: every training sequence comes from a randomly sampled recurrent causal model, so each one is a new…

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