Enhancing Natural Language Instruction Document Comprehension with Large Language Models
Shang Li, Yang Chen, Xin Zhang · 2024
Recent advancements in Large Language Models (LLMs) have created new avenues for automated understanding and analysis of natural language instruction documents. This paper presents an innovative approach that leverages pre-trained LLMs with specialized fine-tuning techniques to facilitate understanding and analysis of instruction documents across a range of domains. Our method capitalizes on the semantic understanding capabilities of LLMs, adapting to instruction comprehension tasks through few-shot learning and prompt engineering. The model demonstrates proficiency in identifying key steps, entities, and their logical relationships within instructions, exhibiting robust cross-domain generalization capabilities. A multi-domain instruction document dataset is constructed to evaluate the model’s performance. Experimental results indicate excellent performance across a range of instruction comprehension tasks, including step sequencing, entity recognition, and contextual interpretation. This research contributes to advancing document comprehension using LLMs, with the potential for significant impact on technical documentation management, intelligent customer service, and automated workflow processes.