Version 1
: Received: 25 December 2023 / Approved: 26 December 2023 / Online: 26 December 2023 (11:01:25 CET)
How to cite:
Ouyang, Q.; Wang, S.; Wang, B. Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection. Preprints2023, 2023121987. https://doi.org/10.20944/preprints202312.1987.v1
Ouyang, Q.; Wang, S.; Wang, B. Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection. Preprints 2023, 2023121987. https://doi.org/10.20944/preprints202312.1987.v1
Ouyang, Q.; Wang, S.; Wang, B. Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection. Preprints2023, 2023121987. https://doi.org/10.20944/preprints202312.1987.v1
APA Style
Ouyang, Q., Wang, S., & Wang, B. (2023). Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection. Preprints. https://doi.org/10.20944/preprints202312.1987.v1
Chicago/Turabian Style
Ouyang, Q., Shiyu Wang and Bing Wang. 2023 "Enhancing Accuracy in Large Language Models Through Dynamic Real-Time Information Injection" Preprints. https://doi.org/10.20944/preprints202312.1987.v1
Abstract
This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.
Keywords
Large Language Models; Real-Time Data Integration; Content Hallucination; Alpaca Model; Accuracy Improvement; Dynamic Data Processing
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.
