Complementary Learning Approach for Text Classification using Large Language Models
Abstract
In this study, we propose a structured methodology that utilizes large language models (LLMs) in a cost-efficient and parsimonious manner, integrating the strengths of scholars and machines while offsetting their respective weaknesses. Our methodology, facilitated through a chain of thought and few-shot learning prompting from computer science, extends best practices for co-author teams in qualitative research to human-machine teams in quantitative research. This allows humans to utilize abductive reasoning and natural language to interrogate not just what the machine has done but also what the human has done. Our method highlights how scholars can manage inherent weaknesses OF LLMs using careful, low-cost techniques. We demonstrate how to use the methodology to interrogate human-machine rating discrepancies for a sample of 1,934 press releases announcing pharmaceutical alliances (1990-2017).
Cite
@article{arxiv.2512.07583,
title = {Complementary Learning Approach for Text Classification using Large Language Models},
author = {Navid Asgari and Benjamin M. Cole},
journal= {arXiv preprint arXiv:2512.07583},
year = {2025}
}
Comments
After further review, we identified substantive issues that materially affect the validity of the manuscript's core results and conclusions. Addressing these would require a fundamental reworking of the analysis and framing. To maintain the integrity of the public record, we request withdrawal of this version