Transformers can perform in-context data generation, simulating generative samplers

Researchers have demonstrated that large language models, specifically transformers, can function as in-context samplers for data generation tasks. The study proves that these models can simulate iterative generative samplers, extending in-context learning beyond supervised learning to data generation. This is achieved by identifying a generative role for softmax attention in computing responsibility weights and empirical averages, while feedforward layers perform Euler updates. Empirically, transformers were shown to approximate energy-based samplers when presented with prompts related to specific semantic topics. AI

IMPACT Demonstrates a novel capability for LLMs in data generation, potentially impacting generative AI applications.

RANK_REASON The cluster contains a research paper detailing theoretical and empirical findings about transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• diffusion
• Gotit.pub
• Hugging Face
• IArxiv
• Leonhard Euler
• ScienceCast
• Softmax
• transformers

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