Version 1
: Received: 6 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (08:38:17 CEST)
How to cite:
Zhang, J. Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical Texts. Preprints2024, 2024100470. https://doi.org/10.20944/preprints202410.0470.v1
Zhang, J. Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical Texts. Preprints 2024, 2024100470. https://doi.org/10.20944/preprints202410.0470.v1
Zhang, J. Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical Texts. Preprints2024, 2024100470. https://doi.org/10.20944/preprints202410.0470.v1
APA Style
Zhang, J. (2024). Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical Texts. Preprints. https://doi.org/10.20944/preprints202410.0470.v1
Chicago/Turabian Style
Zhang, J. 2024 "Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical Texts" Preprints. https://doi.org/10.20944/preprints202410.0470.v1
Abstract
The advent of Large Language Models (LLMs) has significantly advanced natural language processing (NLP), yet their application to historical texts remains challenging due to archaic language, distinct terminologies, and varied contextual backgrounds. This study introduces Historical Domain Large Language Models, designed to bridge this gap by adapting LLMs for better comprehension and processing of historical data. Our approach leverages context-aware and task-specific prompts to enhance model performance in tasks such as named entity recognition (NER), sentiment analysis, and information extraction within historical contexts. We propose an iterative refinement process to improve prompt quality and model outputs continuously. Instruction tuning on newly collected evaluation data ensures our methods' efficacy, avoiding biases from previously used datasets. Evaluations using GPT-4 demonstrate significant improvements in handling historical texts, underscoring the potential of our approach to unlock profound insights from historical data. This work highlights the importance of tailored LLM adaptations for specialized domains, offering a robust framework for future research in historical NLP.
Keywords
Iterative Refinement; Natural Language 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.