Legal Data Augmentation in LLMs for Low-resourced Domains

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Legal Data Augmentation in LLMs for Low-resourced Domains"

By

Mr. Tsz Ho LI


Abstract:

Large language models (LLMs) have currently enhanced their reasoning 
capabilities. Recent work aims to leverage reasoning skills in the legal 
domain, providing legal services and developing legal frameworks using LLMs. 
However, these works primarily focus on major jurisdictions with 
well-annotated datasets (e.g., the US, China, and the EU), while legal 
domains with less-annotated data are rarely explored. In these under- 
represented regions, data scarcity limits the viability and application of 
existing legal frameworks. In this thesis, we introduce CLAug, a cross- 
regional legal data augmentation method, to address the data scarcity in 
low-resourced legal domains. Our method uses foreign legal data, particularly 
from major jurisdictions, as augmentation sources in the local legal context. 
We process foreign data through a construction pipeline, so we can treat 
these court cases as if they happened locally for augmentation by 
transferring legal information from a foreign region to a local region. Using 
the existing court cases of the European Union about privacy and AI safety, 
we demonstrate effective legal knowledge augmentation on the Hong Kong 
privacy law domain, under the Hong Kong Personal Data (Privacy) Ordinance 
(PDPO), by in-context learning experiments using LLMs. Our results show that 
LLMs can effectively learn from the augmented data and perform more accurate 
legal judgments in the local context, showing the advantages of data 
augmentation. We also observe two key factors that are critical to the 
augmentation process: diversity from augmentation sources and locality from 
original local data. These findings suggest that legal data augmentation 
using well- annotated data is a promising approach to resolve the data 
scarcity in low-resourced domains.


Date:                   Monday, 17 August 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lift 25/26

Chairman:               Prof. Song GUO

Committee Members:      Dr. Yangqiu SONG (Supervisor)
                        Dr. Dan XU