CLMN: Concept based Language Models via Neural Symbolic Reasoning

1. 1Identify key concepts and their relationships in a given text
2. 2Use neural symbolic reasoning to represent these concepts and their interactions
3. 3Train the CLMN model to predict text based on these concept representations
4. 4Evaluate the model's performance on tasks that require interpretability, such as text classification and question answering

NLP researchers and AI engineers on a team can benefit from CLMN as it provides a more interpretable and transparent language model, allowing for better understanding and control of the model's decisions

💡 CLMN improves interpretability in NLP by tying predictions to human concepts and modeling dynamic concept interactions

💡 Introducing CLMN: a concept-based language model that uses neural symbolic reasoning for more interpretable NLP!