Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model

• URL: http://arxiv.org/abs/2305.13014v4
• Date: Mon, 11 Dec 2023 08:52:35 GMT
• Title: Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model
• Authors: Stefano De Paoli
• Abstract summary: The paper presents results and reflection of an experiment done to use the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic Analysis. The objective of the paper is not to replace human analysts in qualitative analysis but to learn if some elements of LLM data manipulation can to an extent be of support for qualitative research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions which can be applied to several fields and areas of work. This paper presents results and reflection of an experiment done to use the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic Analysis. Previous research on this subject has largely worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations made by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. Attempting an analysis based on human interpretation with an LLM clearly is a provocation but also a way to learn something about how these systems can or cannot be used in qualitative research. The paper presents the motivations for attempting this emulation, it reflects on how the six steps to a Thematic Analysis proposed by Braun and Clarke can at least partially be reproduced with the LLM and it also reflects on what are the outputs produced by the model. The paper used two existing datasets of open access semi-structured interviews, previously analysed with Thematic Analysis by other researchers. It used the previously produced analysis (and the related themes) to compare with the results produced by the LLM. The results show that the model can infer at least partially some of the main Themes. The objective of the paper is not to replace human analysts in qualitative analysis but to learn if some elements of LLM data manipulation can to an extent be of support for qualitative research.

Related papers

• Interactive Topic Models with Optimal Transport [75.26555710661908]
  We present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
  arXiv  Detail & Related papers  (2024-06-28T13:57:27Z)
• Categorical Syllogisms Revisited: A Review of the Logical Reasoning Abilities of LLMs for Analyzing Categorical Syllogism [62.571419297164645]
  This paper provides a systematic overview of prior works on the logical reasoning ability of large language models for analyzing categorical syllogisms. We first investigate all the possible variations for the categorical syllogisms from a purely logical perspective. We then examine the underlying configurations (i.e., mood and figure) tested by the existing datasets.
  arXiv  Detail & Related papers  (2024-06-26T21:17:20Z)
• Automating Thematic Analysis: How LLMs Analyse Controversial Topics [5.025737475817937]
  Large Language Models (LLMs) are promising analytical tools. This paper explores how LLMs can support thematic analysis of controversial topics. Our findings highlight intriguing overlaps and variances in thematic categorisation between human and machine agents.
  arXiv  Detail & Related papers  (2024-05-11T05:28:25Z)
• QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums [10.684484559041284]
  This study introduces QuaLLM, a novel framework to analyze and extract quantitative insights from text data on online forums. We applied this framework to analyze over one million comments from two Reddit's rideshare worker communities.
  arXiv  Detail & Related papers  (2024-05-08T18:20:03Z)
• Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis [6.592797748561459]
  Large Language Models (LLMs) have enabled collaborative human-bot interactions in Software Engineering (SE) We introduce a new dimension of scalability and accuracy in qualitative research, potentially transforming data interpretation methodologies in SE.
  arXiv  Detail & Related papers  (2024-02-02T13:10:46Z)
• Zero-shot Causal Graph Extrapolation from Text via LLMs [50.596179963913045]
  We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. We extend our approach to extrapolating causal graphs through iterated pairwise queries.
  arXiv  Detail & Related papers  (2023-12-22T13:14:38Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• "You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation [60.863629647985526]
  We examine the successes and limitations of the GPT-3, ChatGPT, and GPT-4 models in analysis of sentence meaning structure. We find that models can reliably reproduce the basic format of AMR, and can often capture core event, argument, and modifier structure. Overall, our findings indicate that these models out-of-the-box can capture aspects of semantic structure, but there remain key limitations in their ability to support fully accurate semantic analyses or parses.
  arXiv  Detail & Related papers  (2023-10-26T21:47:59Z)
• Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence [0.0]
  Large language models (LLMs) have been shown to present an unprecedented opportunity to scale up data analytics in the humanities and social sciences. We build on mixed methods quantitizing and converting design principles, and feature analysis from linguistics, to transparently integrate human expertise and machine scalability. The approach is discussed and demonstrated in over a dozen LLM-assisted case studies, covering 9 diverse languages, multiple disciplines and tasks.
  arXiv  Detail & Related papers  (2023-09-24T14:21:50Z)
• Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity [1.7947441434255664]
  Large-scale generative Language Models (LLMs) can simulate free responses to interview questions like those traditionally analyzed using qualitative research methods. Here we consider whether artificial "silicon participants" generated by LLMs may be productively studied using qualitative methods.
  arXiv  Detail & Related papers  (2023-09-06T15:00:44Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.