As LLMs become integral to decision support systems, it’s crucial to understand the determinants of LLM-assisted decision-making. The paper ‘Determinants of LLM-assisted Decision-Making’ by Eva Eigner and Thorsten Händler delves into this topic through a comprehensive literature analysis.

• Explores technological, psychological, and decision-specific determinants impacting decision-making with LLM support.
• Highlights the importance of transparency, prompt engineering, emotions, decision styles, task difficulty, and accountability.
• Presents a dependency framework for interdependencies between various determinants.
• Underlines significant aspects like trust, mental models, and information processing characteristics in LLM-assisted decisions.

This study sheds light on the complex interplay between technology, psychology, and task-specific factors in LLM-enhanced decision-making. Understanding these determinants can lead to more effective human-AI collaborations, empowering users and organizations to make better-informed decisions and design more efficient LLM interfaces.