Summary of Scalable Qualitative Coding with Llms: Chain-of-thought Reasoning Matches Human Performance in Some Hermeneutic Tasks, by Zackary Okun Dunivin
Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Tasks
by Zackary Okun Dunivin
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As researchers explore the potential of large language models (LLMs) in automating qualitative coding, our study investigates whether these AI systems can accurately interpret text and apply category labels to dense passages representative of humanistic studies. We find that GPT-4, a state-of-the-art LLM, demonstrates human-equivalent interpretations for three out of nine codes and substantial reliability for eight out of nine codes when prompted to provide rationale justifying its coding decisions. In contrast, GPT-3.5 underperforms for all codes. Our results suggest that certain codebooks may be suitable for AI coding with the next generation of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer programs called large language models (LLMs) to help people do research by analyzing text. These LLMs can read and understand text, just like humans do! The study looked at how well these LLMs can do this job by comparing their results with what a human researcher would do. They found that the best LLM, GPT-4, is very good at understanding text and applying category labels to long passages of writing. This means that in the future, AI might be able to help researchers with tasks like coding, freeing up humans to focus on more creative work. |
Keywords
* Artificial intelligence * Gpt
