SPECIFIC WAYS TO SOLVE THE PROBLEM OF INSUFFICIENT UNIQUE DATA FOR TRAINING NEURAL NETWORKS IN JURISPRUDENCE
Keywords:
generative adversarial networks (GAN), Data Augmentation for Low-Resource Legal NLP, large language models (LLM), variational autoencoders (VAE), GDPR, Legal-BERTAbstract
Neural networks and machine learning are increasingly being integrated into various legal domains, including document analysis, legal research, and predicting court case outcomes. This is driven by the desire to automate routine tasks and improve the efficiency of legal practice. However, a fundamental obstacle to training reliable and accurate models in jurisprudence is the problem of insufficient unique and labeled data. The legal field, with its complexity and need for expert labeling, inherently suffers from data scarcity more than some other fields. Using incorrect or insufficient data can lead to legal risks, biases, and decreased model quality, highlighting the importance of data uniqueness and high quality. This article will discuss several specific methodologies aimed at solving the problem of insufficient unique data for training neural networks in jurisprudence: data augmentation, synthetic data generation, transfer learning, and few-shot learning.
References
The Rise of AI in Legal Practice: Opportunities, Challenges, & Ethical Considerations, By Joely Williamson / March 21, 2025, https://ctlj.colorado.edu/?p=1297
Overview of Synthetic Data Generation Methods, By Paul Pokotylo, Oct 21, 2024, https://keymakr.com/blog/overview-of-synthetic-data-generation-methods/
Zero-Shot and Few-Shot Learning with LLMs, by Michał Oleszak, 25th September, 2024, https://neptune.ai/blog/zero-shot-and-few-shot-learning-with-llms
Bringing transparency to the data used to train artificial intelligence, by Beth Stackpole, Mar 3, 2025, https://mitsloan.mit.edu/ideas-made-to-matter/bringing-transparency-to-data-used-to-train-artificial-intelligence
