Advanced Chain-of-Thought Reasoning for Parameter Extraction from Documents Using Large Language Models

• URL: http://arxiv.org/abs/2502.16540v1
• Date: Sun, 23 Feb 2025 11:19:44 GMT
• Title: Advanced Chain-of-Thought Reasoning for Parameter Extraction from Documents Using Large Language Models
• Authors: Hong Cai Chen, Yi Pin Xu, Yang Zhang,
• Abstract summary: Current methods struggle to handle high-dimensional design data and meet the demands of real-time processing.<n>We propose an innovative framework that automates the extraction of parameters and the generation of PySpice models.<n> Experimental results show that applying all three methods together improves retrieval precision by 47.69% and reduces processing latency by 37.84%.
• Score: 3.7324910012003656
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extracting parameters from technical documentation is crucial for ensuring design precision and simulation reliability in electronic design. However, current methods struggle to handle high-dimensional design data and meet the demands of real-time processing. In electronic design automation (EDA), engineers often manually search through extensive documents to retrieve component parameters required for constructing PySpice models, a process that is both labor-intensive and time-consuming. To address this challenge, we propose an innovative framework that leverages large language models (LLMs) to automate the extraction of parameters and the generation of PySpice models directly from datasheets. Our framework introduces three Chain-of-Thought (CoT) based techniques: (1) Targeted Document Retrieval (TDR), which enables the rapid identification of relevant technical sections; (2) Iterative Retrieval Optimization (IRO), which refines the parameter search through iterative improvements; and (3) Preference Optimization (PO), which dynamically prioritizes key document sections based on relevance. Experimental results show that applying all three methods together improves retrieval precision by 47.69% and reduces processing latency by 37.84%. Furthermore, effect size analysis using Cohen's d reveals that PO significantly reduces latency, while IRO contributes most to precision enhancement. These findings underscore the potential of our framework to streamline EDA processes, enhance design accuracy, and shorten development timelines. Additionally, our algorithm has model-agnostic generalization, meaning it can improve parameter search performance across different LLMs.

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