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Anand, Y., Nussbaum, Z., Duderstadt, B., Schmidt, B. and Mulyar, A. (2023) GPT4All: Training an Assistant-Style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo.
https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf

has been cited by the following article:

• TITLE: Storyline Extraction of Document-Level Events Using Large Language Models

  AUTHORS: Ziyang Hu, Yaxiong Li

  KEYWORDS: Document-Level Storyline Extraction, Timeline, Large Language Models, Topological Structure of Storyline, Prompt Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.11, November 27, 2024

  ABSTRACT: This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.