A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?
A.
Build an automatic named entity recognition system.
C: Develop a summarization chatbot.
Explanation:
A summarization chatbot powered by large language models (LLMs) can read and analyze legal documents to extract key points. This aligns with the law firm’s requirement to process complex documents and provide concise summaries of the critical information.
Named entity recognition (NER)—also called entity chunking or entity extraction—is a component of natural language processing (NLP) that identifies predefined categories of objects in a body of text.
These categories can include, but are not limited to, names of individuals, organizations, locations, expressions of times, quantities, medical codes, monetary values and percentages, among others. Essentially, NER is the process of taking a string of text (i.e., a sentence, paragraph or entire document), and identifying and classifying the entities that refer to each category.
C. Develop a summarization chatbot.
Explanation:
A summarization chatbot can leverage large language models (LLMs) to automatically read and extract key points from legal documents by summarizing the content. This approach aligns well with the firm's need to condense lengthy documents into concise, relevant summaries, making it easier for users to quickly understand the main points without reading the entire document. LLMs are highly effective at summarization tasks, especially when fine-tuned on domain-specific data like legal text.
Building an AI-powered web application with document summarization and chatbot features can significantly enhance user experience by providing quick, relevant insights and interactive support
Great suggestion on the chatbot! p2pcerts looks like a solid platform for it.
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