phosseini/causal-reasoning
Knowledge-Augmented Language Models for Cause-Effect Relation Classification https://arxiv.org/abs/2112.08615
This project helps researchers and data scientists improve how language models understand cause-and-effect relationships from text. It takes structured knowledge graphs like ATOMIC2020 or GLUCOSE, converts them into natural language sentences, and then uses these sentences to further train language models like BERT or RoBERTa. The outcome is a language model better equipped to identify causal links in new text, useful for those working with large text datasets to extract insights.
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Use this if you are a researcher or data scientist needing to enhance a language model's ability to accurately classify cause-effect relationships within text for tasks like logical reasoning or predictive analytics.
Not ideal if you are looking for a plug-and-play solution for general text classification without a specific focus on causal inference or if you lack expertise in training and fine-tuning large language models.
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Jun 14, 2023
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