Spin glass model of in-context learning
<|reference_start|>Spin glass model of in-context learning: Large language models show a surprising in-context learning ability -- being able to use a prompt to form a prediction for a query, yet without additional training, in stark contrast to old-fashioned supervised learning. Providing a mechanistic interpretation and linking the empirical phenomenon to physics are thus challenging and remain unsolved. We study a simple yet expressive transformer with linear attention, and map this structure to a spin glass model with real-valued spins, where the couplings and fields explain the intrinsic disorder in data. The spin glass model explains how the weight parameters interact with each other during pre-training, and most importantly why an unseen function can be predicted by providing only a prompt yet without training. Our theory reveals that for single instance learning, increasing the task diversity leads to the emergence of the in-context learning, by allowing the Boltzmann distribution to converge to a unique correct solution of weight parameters. Therefore the pre-trained transformer displays a prediction power in a novel prompt setting. The proposed spin glass model thus establishes a foundation to understand the empirical success of large language models.<|reference_end|>
@article{li2024spin,
    title={Spin glass model of in-context learning},
    author={Yuhao Li, Ruoran Bai, Haiping Huang},
    journal={arXiv preprint arXiv:2408.02288},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02288},
    primaryClass={cond-mat.dis-nn cond-mat.stat-mech cs.AI cs.CL}
}