LAG-XAI: A Lie-Inspired Affine Geometric Framework for Interpretable Paraphrasing in Transformer Latent Spaces

1. 1Conceptualize paraphrasing as a structured affine transformation within the embedding space
2. 2Model paraphrasing using Lie-inspired affine geometry
3. 3Apply LAG-XAI to Transformer-based language models to improve interpretability of latent semantic spaces
4. 4Evaluate the effectiveness of LAG-XAI in various NLP tasks

NLP researchers and AI engineers benefit from LAG-XAI as it provides a novel approach to understanding and interpreting paraphrasing in language models, enabling them to develop more transparent and explainable AI systems

💡 Paraphrasing can be modeled as a structured affine transformation within the embedding space, enabling more interpretable and explainable AI systems

💡 LAG-XAI: A novel geometric framework for interpretable paraphrasing in Transformer latent spaces