This work proposes a retrieval-augmented LLM pipeline that identifies and optimizes problematic contract revisions using synthetic data, semantic retrieval, and acceptability-based alignment.
We propose a retrieval-augmented LLM system that automatically identifies and rewrites problematic industrial contract revisions, achieving high accuracy even in low-resource legal settings.
We introduce gradient-norm–weighted averaging methods for distributed deep learning that pull workers toward flatter regions of the loss landscape. The proposed model-level and layer-level variants converge in both convex and non-convex settings and empirically achieve faster training, better optima, and reduced communication compared to prior work.
We propose an automated, bi-level document classification pipeline for regulatory text that first filters irrelevant documents using an ensemble of classical classifiers and then routes relevant documents to the appropriate departments using a transformer-based DocBERT model, significantly improving efficiency and accuracy over single-model approaches when evaluated on a large real-world corpus from Con Edison.