Decision-making is a fundamental capability in everyday life. Large Language
Models (LLMs) provide multifaceted support in enhancing human decision-making
processes. However, understanding the influencing factors of LLM-assisted
decision-making is crucial for enabling individuals to utilize LLM-provided
advantages and minimize associated risks in order to make more informed and
better decisions. This study presents the results of a comprehensive literature
analysis, providing a structural overview and detailed analysis of determinants
impacting decision-making with LLM support. In particular, we explore the
effects of technological aspects of LLMs, including transparency and prompt
engineering, psychological factors such as emotions and decision-making styles,
as well as decision-specific determinants such as task difficulty and
accountability. In addition, the impact of the determinants on the
decision-making process is illustrated via multiple application scenarios.
Drawing from our analysis, we develop a dependency framework that systematizes
possible interactions in terms of reciprocal interdependencies between these
determinants. Our research reveals that, due to the multifaceted interactions
with various determinants, factors such as trust in or reliance on LLMs, the
user's mental model, and the characteristics of information processing are
identified as significant aspects influencing LLM-assisted decision-making
processes. Our findings can be seen as crucial for improving decision quality
in human-AI collaboration, empowering both users and organizations, and
designing more effective LLM interfaces. Additionally, our work provides a
foundation for future empirical investigations on the determinants of
decision-making assisted by LLMs.

通过对大型语言模型辅助决策的影响因素进行综合文献分析，本研究提供了一种结构概述和详细分析，探讨了技术、心理和决策特定的决策影响因素，同时通过多个应用场景展示了这些影响因素对决策过程的影响，并提出了一种依赖框架来系统化这些因素之间的相互作用。研究发现，信任或依赖语言模型、用户的心理模型以及信息处理的特征是影响语言模型辅助决策过程的重要方面，这些发现对于提高人工智能合作中的决策质量、增强用户和组织的能力以及设计更有效的语言模型接口至关重要，并为后续对语言模型辅助决策的决策影响因素的实证研究奠定了基础。

LLM 辅助决策的决定因素

Determinants of LLM-assisted Decision-Making

For text-level discourse analysis, there are various discourse schemes but
relatively few labeled data, because discourse research is still immature and
it is labor-intensive to annotate the inner logic of a text. In this paper, we
attempt to unify multiple Chinese discourse corpora under different annotation
schemes with discourse dependency framework by designing semi-automatic methods
to convert them into dependency structures. We also implement several benchmark
dependency parsers and research on how they can leverage the unified data to
improve performance.

本文旨在建立一个语义依赖关系框架，并研究基于此框架的语料库进行多元语言分析，为依步性分析提供数据基础。