# A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification

arXiv: https://arxiv.org/abs/2603.09990 · HTML: https://mlsanity.com/paper/2603.09990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 20 of 20)

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## Abstract (arXiv)

In business-to-business relations, it is common to establish NonDisclosure Agreements (NDAs). However, these documents exhibit significant variation in format, structure, and writing style, making manual analysis slow and error-prone. We propose an architecture based on LLMs to automate the segmentation and clauses classification within these contracts. We employed two models: LLaMA-3.1-8B-Instruct for NDA segmentation (clause extraction) and a fine-tuned Legal-Roberta-Large for clause classification. In the segmentation task, we achieved a ROUGE F1 of 0.95 +/- 0.0036; for classification, we obtained a weighted F1 of 0.85, demonstrating the feasibility and precision of the approach.