Leveraging Large Language Models for Thematic Analysis in Italian: A Feasibility Study

Core Concepts

Large Language Models can effectively perform inductive Thematic Analysis on qualitative data in languages other than English, such as Italian.

Abstract

The paper presents a test of using Large Language Models (LLMs), specifically GPT3.5-Turbo and GPT4-Turbo, to perform Thematic Analysis (TA) on a dataset of semi-structured interviews in Italian.
The key highlights and insights are:

The LLMs were able to generate high-quality initial codes from the Italian interview data, with descriptive names, meaningful descriptions, and illustrative quotes.
The LLMs then successfully identified 9 themes from the initial codes, which showed good semantic similarity to the original categories identified by human researchers using Grounded Theory.
A comparison of codes generated using prompts in Italian versus English showed the LLMs produced similar results, indicating the models can work effectively with either language.
The lead author of the original research provided high scores (8-10 out of 10) on how well the LLM-generated themes captured the meaning of the original categories.
These results demonstrate the potential of LLMs to support qualitative analysis in languages beyond just English, which has important implications for enabling multilingual research and analysis. The methodology used in this study, building on the author's previous work, provides a robust approach for leveraging LLMs for inductive Thematic Analysis.

Stats

"The sector is opening up with great interest, including experimental research on the possibility of digital editions."
"There are certainly privacy aspects in the data: when collecting data about individuals, this aspect is present, and there is a dedicated task to address this in the project."
"Sharing takes place, but for example, in the proofreading phase, so typically we read things among ourselves."

Quotes

"The critical edition of a manuscript can be considered as data."
"The texts of the critical edition can be considered data."
"The work of translators should be better recognized and valued."

Key Insights Distilled From

Thematic Analysis with Large Language Models: does it work with languages other than English? A targeted test in Italian

by Stefano De P... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08488.pdf

Deeper Inquiries

How can the multilingual capabilities of LLMs be further leveraged to support qualitative research in diverse global contexts?

The multilingual capabilities of Large Language Models (LLMs) can significantly enhance qualitative research in diverse global contexts by enabling researchers to analyze data in multiple languages without the need for translation. This capability allows for a more inclusive approach to research, especially in international projects where data is collected in various languages. Researchers can conduct analyses directly in the language of the data, preserving the nuances and cultural context that may be lost in translation. This not only saves time and resources but also ensures that the analysis is more authentic and accurate.
Furthermore, leveraging the multilingual capabilities of LLMs can facilitate cross-cultural research collaborations by enabling researchers from different linguistic backgrounds to work together seamlessly. It promotes diversity and inclusivity in research practices, allowing for a more comprehensive understanding of global issues and perspectives. Additionally, it opens up opportunities for researchers to explore new research questions and areas that may be specific to certain languages or regions.

What are the potential limitations or biases that may arise when using LLMs for qualitative analysis in languages other than the model's primary training data?

While the multilingual capabilities of LLMs offer numerous benefits, there are potential limitations and biases that researchers need to be aware of when using these models for qualitative analysis in languages other than the model's primary training data. Some of these limitations and biases include:

Limited Training Data: LLMs may not have been trained on as much data in languages other than the primary language, leading to potential inaccuracies or biases in the analysis.

Cultural Nuances: LLMs may struggle to capture subtle cultural nuances and context-specific meanings present in languages other than the primary one, which could impact the accuracy of the analysis.

Translation Errors: If the model relies on automated translation for prompts or data in different languages, there is a risk of translation errors that could introduce inaccuracies or misinterpretations in the analysis.

Bias in Training Data: LLMs may inherit biases present in the training data, which could be amplified when analyzing data in languages with less training data available, leading to skewed results.

Language Complexity: Some languages may have unique grammatical structures, idiomatic expressions, or linguistic features that LLMs may struggle to interpret accurately, affecting the quality of the analysis.

Researchers should be cautious and critically evaluate the results obtained from LLMs when conducting qualitative analysis in languages other than the model's primary training data to mitigate these limitations and biases.

How might the integration of LLM-supported qualitative analysis tools impact the field of digital humanities and the ways in which scholars engage with textual and cultural data?

The integration of LLM-supported qualitative analysis tools has the potential to revolutionize the field of digital humanities and transform the way scholars engage with textual and cultural data. Some of the key impacts include:

Efficiency and Scalability: LLMs can automate and streamline the qualitative analysis process, making it more efficient and scalable. Researchers can analyze large volumes of textual data quickly and accurately, allowing for more comprehensive and in-depth analyses.

Cross-Linguistic Analysis: LLMs enable scholars to conduct cross-linguistic analysis without the need for extensive language expertise or translation services. This promotes inclusivity and diversity in research by facilitating the analysis of data in multiple languages.

Enhanced Insights: By leveraging the natural language processing capabilities of LLMs, scholars can gain deeper insights into textual and cultural data, uncovering patterns, themes, and connections that may not be immediately apparent through traditional analysis methods.

Interdisciplinary Collaboration: LLM-supported qualitative analysis tools can facilitate interdisciplinary collaboration by providing a common platform for researchers from different fields to analyze and interpret textual data. This fosters a more holistic and integrated approach to research in the digital humanities.

Innovation and Exploration: The integration of LLMs in qualitative analysis opens up new possibilities for innovative research methodologies and exploration of complex research questions. Scholars can push the boundaries of traditional research practices and discover novel insights in textual and cultural data.

Overall, the integration of LLM-supported qualitative analysis tools has the potential to enhance the research capabilities of scholars in the field of digital humanities, enabling them to conduct more sophisticated analyses and uncover new knowledge in textual and cultural data.