Submission Type: Regular Short Paper
Submission Track: NLP Applications
Submission Track 2: Human-Centered NLP
Keywords: thematic analysis, NLP applications, qualitative research
TL;DR: We propose a Human-LLM collaboration framework for thematic analysis.
Abstract: Thematic analysis (TA) has been widely used
for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis,
the same piece of data is typically assigned to at least two human coders. Moreover, to produce
meaningful and useful analysis, human coders
develop and deepen their data interpretation
and coding over multiple iterations, making TA
labor-intensive and time-consuming. Recently
the emerging field of large language models
(LLMs) research has shown that LLMs have
the potential replicate human-like behavior in
various tasks: in particular, LLMs outperform
crowd workers on text-annotation tasks, suggesting an opportunity to leverage LLMs on
TA. We propose a human–LLM collaboration
framework (i.e., LLM-in-the-loop) to conduct
TA with in-context learning (ICL). This framework provides the prompt to frame discussions
with a LLM (e.g., GPT-3.5) to generate the final
codebook for TA. We demonstrate the utility
of this framework using survey datasets on the
aspects of the music listening experience and
the usage of a password manager. Results of
the two case studies show that the proposed
framework yields similar coding quality to that
of human coders but reduces TA’s labor and
time demands.
Submission Number: 1081
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