Creating human-like large language model (LLM) agents is crucial for faithful
social simulation. Having LLMs role-play based on demographic information
sometimes improves human likeness but often does not. This study assessed
whether LLM alignment with human behavior can be improved by integrating
information from empirically-derived human belief networks. Using data from a
human survey, we estimated a belief network encompassing 18 topics loading on
two non-overlapping latent factors. We then seeded LLM-based agents with an
opinion on one topic, and assessed the alignment of its expressed opinions on
remaining test topics with corresponding human data. Role-playing based on
demographic information alone did not align LLM and human opinions, but seeding
the agent with a single belief greatly improved alignment for topics related in
the belief network, and not for topics outside the network. These results
suggest a novel path for human-LLM belief alignment in work seeking to simulate
and understand patterns of belief distributions in society.

通过整合从实证人类信念网络中提取的信息，本研究评估了如何改进基于人类行为的大型语言模型（LLM）与人类行为的一致性，结果表明在模拟和理解社会信念分布模式的工作中，将 LLM 代理与单一信念进行关联会显著提高对相关主题的一致性。

超越人口统计学：利用人类信念网络对饰演角色的基于 LLM 的代理进行对齐

Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human  Belief Networks

We consider the human-aware task planning problem where a human-robot team is
given a shared task with a known objective to achieve. Recent approaches tackle
it by modeling it as a team of independent, rational agents, where the robot
plans for both agents' (shared) tasks. However, the robot knows that humans
cannot be administered like artificial agents, so it emulates and predicts the
human's decisions, actions, and reactions. Based on earlier approaches, we
describe a novel approach to solve such problems, which models and uses
execution-time observability conventions. Abstractly, this modeling is based on
situation assessment, which helps our approach capture the evolution of
individual agents' beliefs and anticipate belief divergences that arise in
practice. It decides if and when belief alignment is needed and achieves it
with communication. These changes improve the solver's performance: (a)
communication is effectively used, and (b) robust for more realistic and
challenging problems.

该研究基于情境评估，提出了一种新颖的解决方案，可以在人机团队合作中帮助机器人预测和模拟人类决策，并通过沟通协调达到信念一致，提高问题解决效率和鲁棒性。