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.
The law firm wants to extract key points from legal documents, which aligns with the goal of summarization. A summarization chatbot powered by large language models (LLMs) can read through legal documents and provide concise, accurate summaries that capture the essential points, making it the most appropriate choice.
While useful for identifying specific entities like names, dates, locations, NER doesn't provide comprehensive key point extraction and is too narrow in scope for the stated requirement
Answer "c" is correct. The solution should include the core components to build an AI application that reads legal documents and extracts key points using large language models (LLMs).
Answer "c" is correct. The solution should include the core components to build an AI application that reads legal documents and extracts key points using large language models (LLMs). For more details, see https://l1nq.com/kqsbD
"C" is correct - The primary requirement is to read legal documents and extract key points.
Summarization is the best approach for condensing lengthy legal text into key points while preserving important details.
"A" is incorrect - NER helps identify names, dates, contract numbers. but does not summarize key points from documents.
AWS certification exams are introducing new question types, including ordering, matching, and case study questions, alongside traditional multiple choice and multiple response formats. The ordering type requires arranging selected responses in the correct sequence, while matching questions involve linking statements to prompts. Case studies recycle a scenario across multiple questions, allowing candidates to save time by understanding the context once. Each question is evaluated independently, meaning it's crucial to answer all parts correctly to receive credit.
NER should be more suitable for the legal documents. It is recommended by the Amazon Comprehend docs. When you try to ask an AI Assistant without giving them answers, it will also prefer NER with its advantageous.
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.
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