debjitpaul/Causal_CoT
About The corresponding code from our paper " Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning" . Do not hesitate to open an issue if you run into any trouble!
This project helps developers evaluate and improve how accurately their large language models explain their answers. You provide your model's reasoning steps for a question, along with a 'preferred' and 'dispreferred' set of explanations, and the tool helps train the model to generate more reliable and faithful reasoning. The end-user persona is an AI/ML developer working with advanced language models who needs to ensure their models' explanations are trustworthy.
Use this if you are developing large language models and need a robust framework to make their generated reasoning more transparent and faithful to the actual answer.
Not ideal if you are looking for a plug-and-play solution for general language model fine-tuning without a focus on detailed reasoning fidelity.
Stars
13
Forks
3
Language
Python
License
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Category
Last pushed
Jan 14, 2026
Commits (30d)
0
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