Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data

• URL: http://arxiv.org/abs/2402.00743v2
• Date: Tue, 18 Jun 2024 13:11:32 GMT
• Title: Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
• Authors: Yue Xing, Xiaofeng Lin, Chenheng Xu, Namjoon Suh, Qifan Song, Guang Cheng,
• Abstract summary: Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL) This paper studies the role of each component in the transformer architecture and provides a theoretical understanding to explain the success of the architecture.
• Score: 21.242708937367865
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023trained}, attempt to explain the mechanism of ICL, they assume the input $x_i$ and the output $y_i$ of each demonstration example are in the same token (i.e., structured data). However, in real practice, the examples are usually text input, and all words, regardless of their logic relationship, are stored in different tokens (i.e., unstructured data \cite{wibisono2023role}). To understand how LLMs learn from the unstructured data in ICL, this paper studies the role of each component in the transformer architecture and provides a theoretical understanding to explain the success of the architecture. In particular, we consider a simple transformer with one/two attention layers and linear regression tasks for the ICL prediction. We observe that (1) a transformer with two layers of (self-)attentions with a look-ahead attention mask can learn from the prompt in the unstructured data, and (2) positional encoding can match the $x_i$ and $y_i$ tokens to achieve a better ICL performance.

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