A Human-Centered Workflow for Using Large Language Models in Content Analysis
📰 ArXiv cs.AI
Researchers propose a human-centered workflow for using Large Language Models in content analysis via APIs
Action Steps
- Conceptualize LLMs as universal text processing machines
- Employ LLMs via application programming interfaces (APIs)
- Apply LLMs to three content analysis tasks: annotation, information extraction, and text generation
- Evaluate and refine the workflow based on qualitative and quantitative results
Who Needs to Know This
Data scientists and AI engineers on a team can benefit from this workflow to improve content analysis tasks, while product managers can utilize it to develop more efficient content analysis tools
Key Insight
💡 LLMs can be used as universal text processing machines to enhance content analysis tasks
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🤖 Leveraging LLMs for content analysis via APIs can improve efficiency and accuracy
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