The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.
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This content will become publicly available on March 17, 2025
Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method
Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG al- gorithm that enforces adherence toward spe- cific rhetorical relations in an LLM sentence- completion context by a parser-driven decoding scheme that requires no model fine-tuning. The method is validated both with automatic and human evaluation. The code is accessible on GitHub.
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- Award ID(s):
- 2050919
- PAR ID:
- 10519444
- Publisher / Repository:
- aclanthology.org
- Date Published:
- Format(s):
- Medium: X
- Location:
- Hotel Radisson Blu, St. Julians, in Malta
- Sponsoring Org:
- National Science Foundation
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