Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

• URL: http://arxiv.org/abs/2402.01386v1
• Date: Fri, 2 Feb 2024 13:10:46 GMT
• Title: Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis
• Authors: Zeeshan Rasheed, Muhammad Waseem, Aakash Ahmad, Kai-Kristian Kemell, Wang Xiaofeng, Anh Nguyen Duc, Pekka Abrahamsson
• Abstract summary: 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.
• Score: 6.592797748561459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have enabled collaborative human-bot interactions in Software Engineering (SE), similar to many other professions. However, the potential benefits and implications of incorporating LLMs into qualitative data analysis in SE have not been completely explored. For instance, conducting qualitative data analysis manually can be a time-consuming, effort-intensive, and error-prone task for researchers. LLM-based solutions, such as generative AI models trained on massive datasets, can be utilized to automate tasks in software development as well as in qualitative data analysis. To this end, we utilized LLMs to automate and expedite the qualitative data analysis processes. We employed a multi-agent model, where each agent was tasked with executing distinct, individual research related activities. Our proposed model interpreted large quantities of textual documents and interview transcripts to perform several common tasks used in qualitative analysis. The results show that this technical assistant speeds up significantly the data analysis process, enabling researchers to manage larger datasets much more effectively. Furthermore, this approach introduces a new dimension of scalability and accuracy in qualitative research, potentially transforming data interpretation methodologies in SE.

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