Code for "Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs"

1. The generated figures are stores in 'plots' folder. (Please note '*.ipynb' files are used for ploting, and are not executable as they require access to collected evaluation data)
2. The main implementation code can be found in 'scripts' folder. Since we are still gathering experimental evidence, the code is currently in a draft version. A cleaner version of the code will be packaged and released soon.
	a) train.py: Code for training transformer models
	b) test.py: Code for testing pretrained models
	c) tasks.py: Code for generating different datasets/tasks, e.g., 2-linear MLP, multi-layer linear MLP...
	d) models.py: Code for implement transformer models capable of handling different tasks
	e) conf: Configuration files for experimental settings