The paper Determinants of LLM-assisted Decision-Making delves into the nuanced factors that influence human decision-making in the context of Large Language Model (LLM) assistance. Integrating insights from a comprehensive literature analysis, it presents a structured overview of technological, psychological, and decision-specific determinants.

• Addresses model transparency, prompt engineering, and user emotions.
• Considers task difficulty and accountability in decision-making.
• Introduces a dependency framework to analyze determinant interplay.
• Identifies trust, reliance, user mental model, and information processing as key influencers.

This paper is important because it systematizes the multifaceted interactions in LLM-assisted decision-making, facilitating an understanding that can lead to better AI interfaces and empower users. It sets a groundwork for subsequent empirical studies aimed at enhancing decision quality in human-AI collaboration.