arXiv:2507.16003v4 Announce Type: replace Abstract: One of the most striking features of Large Language Models (LLMs) is their ability to learn in-context. Namely at inference time an LLM is able to learn new patterns without any additional weight update when these patterns are presented in the form of examples in the prompt, even if these patterns were not seen during training. The mechanisms through which this can happen are still largely unknown. In this work, we show that the stacking of a self-attention layer with an MLP allows the transformer block to implicitly modify the weights of the
The rapid advancement and widespread deployment of Large Language Models necessitate a deeper understanding of their underlying mechanisms, particularly novel emergent behaviors like in-context learning.
Understanding the implicit dynamics of in-context learning could unlock new capabilities for AI models, reduce reliance on traditional retraining, and accelerate the development of more adaptive AI.
This research provides a foundational theoretical understanding of how LLMs learn from prompts without explicit training, potentially shifting future model design and deployment strategies.
- · AI researchers
- · LLM developers
- · Companies deploying AI agents
- · Academic institutions
More efficient and adaptable AI models requiring less frequent retraining will become feasible.
This efficiency could accelerate the deployment of autonomous AI agents across various industries, enhancing their capabilities without continuous updates.
A deeper understanding of intelligence mechanisms might emerge, influencing the development of artificial general intelligence and its societal integration.
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