kvignesh1420/cot-icl-lab

[ACL 2025] Official implementation of the "CoT-ICL Lab" framework

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This framework helps AI researchers and machine learning engineers study how large language models learn complex reasoning steps from examples. It generates synthetic, tokenized datasets based on customizable graph structures (DAGs) and token configurations. The output is a dataset ready for training and evaluating transformer models, complete with input IDs, attention masks, and chain-of-thought elements. This allows researchers to rigorously test hypotheses about in-context learning.

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Use this if you are a machine learning researcher or engineer who needs to generate controlled synthetic datasets to understand how transformer models perform chain-of-thought reasoning from in-context examples.

Not ideal if you need to work with real-world, non-synthetic datasets or are looking for a general-purpose fine-tuning framework for pre-trained language models.

AI-research NLP-benchmarking synthetic-data-generation transformer-evaluation in-context-learning
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

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Forks

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Language

Python

License

MIT

Last pushed

Oct 10, 2025

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