arXiv AI
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
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Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.
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Transformer-based In-Context Score Operator Learning for Diffusion Models
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Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformers
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Hugging Face Trending Papers
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Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics have so far been mostly tied to specific tasks, we study a generalized class of inductive tasks that unifies several synthetic tasks known in the literature, including in-context n-grams and multi-hop reasoning.
arXiv AI
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LF${}^{2}$AR: Accounting for Layerwise Dynamics to Improve Multimodal Adaptation of Language Models
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