Created By: Amin Zayeromali
Experienced Data Scientist with 16+ Years of Software Engineering Expertise | Machine Learning, Python, Django, NLP, DL Expert | Data-Driven Growth Catalyst | Seeking New Opportunities
This proposal addresses the pressing need for an efficient solution to streamline the semantic analysis of unstructured legal texts faced by governments, legal entities, and professionals. By harnessing the capabilities of artificial intelligence models, particularly large language models (LLMs), our project aims to significantly reduce the time and effort spent on the manual review of legal documents.
In the contemporary legal landscape, the increasing volume of unstructured textual data poses a significant challenge to timely and accurate analysis. This proposal presents a solution that leverages artificial intelligence to enhance the semantic analysis process, focusing on the identification of legal nuances and deviations from established rules.
Legal professionals and government entities grapple with the time-intensive task of processing and understanding vast amounts of unstructured textual data. This creates bottlenecks in the review of legal documents, hindering efficiency and potentially leading to oversights.
- Develop an AI-powered assistant for semantic analysis in the legal domain.
- Distinguish sentences in legal texts based on their alignment with existing regulations.
- Expedite the analysis process through the pre-production of vectors and microservices architecture.
- Ensure the solution is cost-effective and accessible.
Our solution involves the utilization of artificial intelligence models, including large language models, to semantically analyze legal texts. By converting input texts and legal rules into sentences and processing them with predefined rules, the system will highlight synonyms and contrasts within the text, aiding legal professionals in their analysis. Finally, our goal will be to argue these highlights with the analytical power of large language models.
To optimize performance, we propose pre-producing vectors (embeddings) through elastic search and storing them in a database for efficient retrieval. The microservices architecture, based on the Django REST framework, will incorporate a user-friendly interface for text input and result display. Additionally, the system design includes microservices dedicated to large language models, such as SBRT models, and open-source models like BERT, as depicted in the proposed system design diagram.
System Design Diagram:
- Time Efficiency: Addressed through pre-production of law text and elastic search for efficient sentence retrieval.
- Semantic Contrasts: Leveraging the capabilities of large language models to recognize contrasts in legal texts.
- Deployment Costs: Balancing the use of open-source models or API interfaces for cost-effective deployment.
- Improve processing speed for texts requiring semantic reasoning in legal contexts.
- Harness AI capacity and semantic analysis for government, immigration, and legal organizations.
- Focus on analyzing unstructured texts, differentiating from projects dealing with structured texts.
- We aim to prevent "Catastrophic Hallucination" in our large language models by ensuring they are well-informed and capable of avoiding the generation of misleading outputs, particularly in the specialized domain of legal expertise, where we rely on our reputable sources for knowledge.
- Increased processing speed, reducing complications in legal text analysis.
- Pre-analysis of texts, assisting experts in understanding the semantic meaning.
- Time and cost savings through efficient AI-driven analysis.
- Minimized errors in semantic analysis, leveraging machine precision.
Addressing the vulnerability of large language models to "Catastrophic Hallucination" is a crucial aspect of tackling this challenge. One of the primary justifications for undertaking this project is to ensure the interpretability of the AI system, which is essential for establishing trust in the generated outputs, especially in the complex realm of legal analysis.
This proposal outlines a comprehensive and practical solution to the challenges faced in the semantic analysis of legal texts. By integrating artificial intelligence models into the workflow, our project aims to revolutionize the efficiency, accuracy, and accessibility of legal document analysis, benefiting governments, legal entities, and professionals in the field.
Download Proposal PDF File:
Created by: Amin Zayeromali on January 21, 2024