From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research

• URL: http://arxiv.org/abs/2412.14461v1
• Date: Thu, 19 Dec 2024 02:21:41 GMT
• Title: From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research
• Authors: Xiang Cheng, Raveesh Mayya, João Sedoc,
• Abstract summary: This paper introduces the SILICON" (textbfSystematic textbfInference with textbfLLMs for textbfInformation textbfClassificatitextbfon and textbfNotation) workflow. The workflow integrates established principles of human annotation with systematic prompt optimization and model selection. We validate the SILICON workflow through seven case studies covering common management research tasks.
• Score: 13.818244562506138
• License:
• Abstract: Unstructured text data annotation and analysis are fundamental to management research, often relying on human annotators through crowdsourcing platforms. While Large Language Models (LLMs) promise to provide a cost-effective and efficient alternative to human annotation, there lacks a systematic workflow that evaluate when LLMs are suitable or how to proceed with LLM-based text annotation in a reproducible manner. This paper addresses this methodological gap by introducing the ``SILICON" (\textbf{S}ystematic \textbf{I}nference with \textbf{L}LMs for \textbf{I}nformation \textbf{C}lassificati\textbf{o}n and \textbf{N}otation) workflow. The workflow integrates established principles of human annotation with systematic prompt optimization and model selection, addressing challenges such as developing robust annotation guidelines, establishing high-quality human baselines, optimizing prompts, and ensuring reproducibility across LLMs. We validate the SILICON workflow through seven case studies covering common management research tasks, including business proposal evaluation, dialog intent and breakdown analysis, review attribute detection. Our findings highlight the importance of validating annotation guideline agreement, the superiority of expert-developed human baselines over crowdsourced ones, the iterative nature of prompt optimization, and the necessity of testing multiple LLMs. Notably, we propose a regression-based methodology to empirically compare LLM outputs across prompts and models. Our workflow advances management research by establishing reproducible processes for LLM-based annotation that maintain scientific rigor. We provide practical guidance for researchers to effectively navigate the evolving landscape of generative AI tools effectively while maintaining transparency and reproducibility.

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