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…

The primary requirement is to "extract key points from the documents," which is the definition of text summarization. Large language models (LLMs) are exceptionally proficient at abstractive summarization, where the model generates a new, concise text that captures the essential information of the original document. A summarization chatbot provides an interactive interface for users to submit legal documents and receive these key points as a summary, directly addressing the law firm's needs.

1. Manning

C. D. (2023). Lecture 10: Prompting

Instruction-Tuning

and RLHF. Stanford University

CS224N: Natural Language Processing with Deep Learning. The lecture slides explicitly list "Summarization" as a primary task that instruction-tuned large language models are designed to perform. (See slide 15

"Instruction-Tuned Models (e.g.

InstructGPT

T5)").

2. AWS Documentation. (2023). Common use cases for foundation models. Amazon Bedrock User Guide. The official documentation lists "Summarization" as a core use case for foundation models

stating they can "provide a summary of a long document such as an article

book

or paper."

3. Bommasani

R.

et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford University Center for Research on Foundation Models (CRFM). Section 4.1

"Language

" discusses the capabilities of foundation models

including summarization

noting their ability to process and condense information from extensive text. (DOI: https://doi.org/10.48550/arXiv.2108.07258).