Related papers: A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks

A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks

URL: http://arxiv.org/abs/2305.17040v1
Date: Fri, 26 May 2023 15:49:43 GMT
Title: A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Authors: Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth
Abstract summary: We show how a transformer model is able to perform ICL under reasonable assumptions on the pre-training process and the downstream tasks. We establish that this entire procedure is implementable using the transformer mechanism.
Score: 29.764014766305174
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples, without any explicit parameter optimization. Our goal is to explain how a pre-trained transformer model is able to perform ICL under reasonable assumptions on the pre-training process and the downstream tasks. We posit a mechanism whereby a transformer can achieve the following: (a) receive an i.i.d. sequence of examples which have been converted into a prompt using potentially-ambiguous delimiters, (b) correctly segment the prompt into examples and labels, (c) infer from the data a \textit{sparse linear regressor} hypothesis, and finally (d) apply this hypothesis on the given test example and return a predicted label. We establish that this entire procedure is implementable using the transformer mechanism, and we give sample complexity guarantees for this learning framework. Our empirical findings validate the challenge of segmentation, and we show a correspondence between our posited mechanisms and observed attention maps for step (c).

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