Can Transformers Learn Sequential Function Classes In Context?
- URL: http://arxiv.org/abs/2312.12655v2
- Date: Thu, 21 Dec 2023 04:29:24 GMT
- Title: Can Transformers Learn Sequential Function Classes In Context?
- Authors: Ryan Campbell, Emma Guo, Evan Hu, Reya Vir, Ethan Hsiao
- Abstract summary: In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP.
We introduce a novel sliding window sequential function class and employ toy-sized transformers with a GPT-2 architecture to conduct our experiments.
Our analysis indicates that these models can indeed leverage ICL when trained on non-textual sequential function classes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In-context learning (ICL) has revolutionized the capabilities of transformer
models in NLP. In our project, we extend the understanding of the mechanisms
underpinning ICL by exploring whether transformers can learn from sequential,
non-textual function class data distributions. We introduce a novel sliding
window sequential function class and employ toy-sized transformers with a GPT-2
architecture to conduct our experiments. Our analysis indicates that these
models can indeed leverage ICL when trained on non-textual sequential function
classes. Additionally, our experiments with randomized y-label sequences
highlights that transformers retain some ICL capabilities even when the label
associations are obfuscated. We provide evidence that transformers can reason
with and understand sequentiality encoded within function classes, as reflected
by the effective learning of our proposed tasks. Our results also show that the
performance deteriorated with increasing randomness in the labels, though not
to the extent one might expect, implying a potential robustness of learned
sequentiality against label noise. Future research may want to look into how
previous explanations of transformers, such as induction heads and task
vectors, relate to sequentiality in ICL in these toy examples. Our
investigation lays the groundwork for further research into how transformers
process and perceive sequential data.
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