Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
Large language models are latent variable models: Explaining and finding good demonstrations for in-context learning, 2024
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Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
Online In-Context Distillation lets small VLMs gain up to 33% performance with as little as 4% teacher annotations by distilling knowledge through dynamic in-context demonstrations at inference.
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Pre-trained Large Language Models Learn Hidden Markov Models In-context
Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
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Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
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Online In-Context Distillation lets small VLMs gain up to 33% performance with as little as 4% teacher annotations by distilling knowledge through dynamic in-context demonstrations at inference.