Developed Pipelines
A Text Extraction Pipeline was developed to process and extract text from over 432,000 legal documents across various formats, including PDFs, DOCX, TXT, images, and Pages files. The system was designed to ensure high-fidelity extraction that preserved essential legal terminology and document formatting.
An OCR Pipeline was integrated to handle non-digital and scanned files. This enabled comprehensive full-text retrieval from all types of legal documents, ensuring no content was left behind due to format limitations.
A robust Data Cleaning Pipeline was created to normalize and clean the extracted data. This involved removing inconsistencies, redundant information, and formatting errors, resulting in high-quality, LLM-ready datasets.
The Dataset Generation Pipeline took the cleaned data and structured it into formats suitable for supervised fine-tuning of language models. This included the creation of instruction-response pairs tailored to legal tasks.
For LLM Fine-Tuning, multiple language models were fine-tuned using custom legal datasets, leveraging Huggingface libraries and AWS Sagemaker. These models were specifically tailored for various user groups, such as legislative drafters, legal researchers, and compliance officers.
A specialized Prompt Engineering Pipeline was also implemented, designing sophisticated prompting strategies for legal use cases. These included bill drafting, amendment suggestions, summarizing legal arguments, and retrieving precedents.
Finally, a Result Verification and Reference Extraction system was built to cross-verify model outputs with original source documents. It also extracted citations and references to ensure legal accuracy and maintain source traceability.
