LegalEagle
LegalEagle
Welcome to LegalEagle! LegalEagle advances the application of Matryoshka Representation Learning (MRL) to legal information retrieval and processing. Our report demonstrates how MRL, which creates nested embeddings of different dimensions within a single embedding vector, can be effectively fine-tuned for legal domain tasks. Our key findings show that fine-tuned MRL models perform comparably to or better than independently trained fixed-size embedding models across all tested dimensions. Additionally, we developed an adaptive retrieval system that leverages MRL’s nested structure to significantly improve document retrieval efficiency - achieving up to 66% faster retrieval speeds while maintaining high accuracy. When integrated with Retrieval-Augmented Generation (RAG), our system shows strong performance on legal question-answering tasks, with accuracy scaling proportionally to embedding size. Our work demonstrates the potential of MRL for creating more efficient and adaptable legal information systems while maintaining high performance standards.
