Version 1
: Received: 22 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (03:50:52 CEST)
How to cite:
Godbole, A.; George, J. G.; Shandilya, S. Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications. Preprints2024, 2024091734. https://doi.org/10.20944/preprints202409.1734.v1
Godbole, A.; George, J. G.; Shandilya, S. Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications. Preprints 2024, 2024091734. https://doi.org/10.20944/preprints202409.1734.v1
Godbole, A.; George, J. G.; Shandilya, S. Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications. Preprints2024, 2024091734. https://doi.org/10.20944/preprints202409.1734.v1
APA Style
Godbole, A., George, J. G., & Shandilya, S. (2024). Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications. Preprints. https://doi.org/10.20944/preprints202409.1734.v1
Chicago/Turabian Style
Godbole, A., Jabin Geevarghese George and Smitha Shandilya. 2024 "Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications" Preprints. https://doi.org/10.20944/preprints202409.1734.v1
Abstract
The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.
Keywords
Large Language Models; Generative AI; Long-context LLM; Multi-document Understanding; Enterprise Applications
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
