Large language models (LLMS) for legal analysis rag and beyond for optimizing domain adaptation in Portuguese legal domain-advanced retrieval and prompt engineering techniques for retrieval augmented generation in the Portuguese legal domain
Year: 2024
Type: dissertation
Abstract: This study explores RAG systems tailored to the Portuguese legal domain, highlighting challenges in underrepresented languages. Fixed-size chunking strategies, particularly Token Text Splitter, were found to be most effective, while more advanced techniques like Recursive and Semantic splitting showed little benefits. Larger chunk sizes improved retrieval accuracy and answer quality, though the impact of chunk overlap remains inconclusive. Self reflection techniques show promising results, particularly for weaker LLMs. GraphRAG shows promise with faster results than traditional RAG approaches. Reranking techniques can improve retrieval but require larger, diverse datasets. Reasoning-based and zero-shot prompting improve accuracy in multi-hop scenarios. Pre-post translation proved to be an efficient technique for mitigating language bias.
Author: Thaçi, Rita
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204983724