Enhancing human capital management through GPT-driven questionnaire generation
The study addresses a gap in the literature regarding AI-generated surveys for Human Capital Management. A novel dataset of HR questions was developed, and the models were evaluated using different techniques, such as zero-shot and one-shot prompting, to assess their ability to generate diverse, semantically accurate, and instruction-aligned questions.
Oct 18, 2024
Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations
This paper presents a strategy for explainable cross-domain recommendations (CDR) using large language models (LLMs). CDR is challenging due to data sparsity, as it requires extensive labeled data across both source and target domains, which is hard to collect. Our approach leverages the knowledge in LLMs to bridge these domains and provide personalized recommendations.
Oct 8, 2024