Large Language Models and the Elicitation of Tacit Knowledge
A Literature Review to Explore the AI Techniques in Uncovering Tacit Knowledge
Y. Chen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Biswas – Mentor (TU Delft - Web Information Systems)
Ujwal Gadiraju – Graduation committee member (TU Delft - Web Information Systems)
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Abstract
This study explores the application of Large Language Models (LLMs) for eliciting tacit knowledge, a challenging area crucial for enhancing decision-making and innovation in organizations. Using a systematic literature review based on PRISMA workflow, the research assesses the potential of LLMs to bridge the gap between tacit knowledge and its articulation. Findings reveal that LLMs, with their advanced natural language processing capabilities, are suitable for capturing tacit knowledge that is typically inaccessible through traditional methods. This paper concludes that while LLMs hold potential for revolutionizing knowledge elicitation practices, careful consideration of their limitations and ethical use is essential. This research contributes to broader discussions on integrating AI in knowledge management and future directions to optimize LLMs’ utility in practical settings.