Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling
This addresses legal access issues for Bangladesh's population of 180 million, but it is an incremental step as it applies an existing method to new data.
This research tackled the problem of delays, complexity, and high costs in Bangladesh's legal system by developing GPT2-UKIL-EN, a specialized Large Language Model fine-tuned on a new English corpus of Bangladeshi legal documents, with evaluation showing promising results for legal assistance.
Purpose: Bangladesh's legal system struggles with major challenges like delays, complexity, high costs, and millions of unresolved cases, which deter many from pursuing legal action due to lack of knowledge or financial constraints. This research seeks to develop a specialized Large Language Model (LLM) to assist in the Bangladeshi legal system. Methods: We created UKIL-DB-EN, an English corpus of Bangladeshi legal documents, by collecting and scraping data on various legal acts. We fine-tuned the GPT-2 model on this dataset to develop GPT2-UKIL-EN, an LLM focused on providing legal assistance in English. Results: The model was rigorously evaluated using semantic assessments, including case studies supported by expert opinions. The evaluation provided promising results, demonstrating the potential for the model to assist in legal matters within Bangladesh. Conclusion: Our work represents the first structured effort toward building an AI-based legal assistant for Bangladesh. While the results are encouraging, further refinements are necessary to improve the model's accuracy, credibility, and safety. This is a significant step toward creating a legal AI capable of serving the needs of a population of 180 million.