Automated Annotation with Generative AI Requires Validation
Nicholas Pangakis, Samuel Wolken, and Neil Fasching

TL;DR
This paper emphasizes the importance of validating generative AI models like GPT-4 for text annotation tasks, demonstrating a workflow and software to ensure reliable, task-specific performance in social science research.
Contribution
It introduces a validated workflow and software for using LLMs in automated annotation, highlighting the need for task-specific validation to ensure accuracy.
Findings
LLM performance varies significantly across datasets and tasks
Validation improves annotation reliability
Software streamlines LLM deployment for annotation
Abstract
Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty. Because these challenges will persist even as LLM technology improves, we argue that any automated annotation process using an LLM must validate the LLM's performance against labels generated by humans. To this end, we outline a workflow to harness the annotation potential of LLMs in a principled, efficient way. Using GPT-4, we validate this approach by replicating 27 annotation tasks across 11 datasets from recent social science articles in high-impact journals. We find that LLM performance for text annotation is promising but highly contingent on both the dataset and the type of annotation task, which reinforces the necessity to validate on a…
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Taxonomy
TopicsComputational and Text Analysis Methods · Topic Modeling · Natural Language Processing Techniques
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Residual Connection · Label Smoothing · Layer Normalization · Byte Pair Encoding · Softmax · Adam · Absolute Position Encodings
