Instruction Fine-Tuning: The Key to Professional, High-Quality Automated Writing

DOI

10.2991/978-94-6463-512-6_41How to use a DOI?

Keywords

Instruction fine-tuning; Instruction data set; Instruction fine-tuning model

Abstract

This article delves into the application of instruction fine-tuning for enhancing automated writing capabilities. Instruction fine-tuning involves further training a large language model (LLM) on datasets comprising specific instructions and corresponding outputs. This process enhances the model's proficiency in understanding and executing complex, specialized tasks. The paper details various types of instructional datasets, fine-tuning techniques, and exemplary models that have benefitted from this approach. Notably, instruction fine-tuning enables models to generate content that adheres to industry standards, significantly boosting efficiency in professional domains such as technical writing, medicine, and law. The paper also addresses the challenges associated with instruction fine-tuning, including data quality, model adaptability, and the computational resources required. Future prospects highlight the transformative potential of this technique in achieving professional and high-quality automated writing. By refining the ability to follow nuanced instructions, fine-tuned models can revolutionize content generation, making them invaluable tools in specialized fields where precision and quality are paramount. This comprehensive exploration underscores the critical role of instruction fine-tuning in the evolving landscape of automated writing technology.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yihao Guo
PY  - 2024
DA  - 2024/09/23
TI  - Instruction Fine-Tuning: The Key to Professional, High-Quality Automated Writing
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 380
EP  - 392
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_41
DO  - 10.2991/978-94-6463-512-6_41
ID  - Guo2024
ER  -