The emergence of unveiling human-like behaviors in Large Language Models
(LLMs) has led to a closer connection between NLP and human psychology, leading
to a proliferation of computational agents. Scholars have been studying the
inherent personalities displayed by LLM agents and attempting to incorporate
human traits and behaviors into them. However, these efforts have primarily
focused on commercially-licensed LLMs, neglecting the widespread use and
notable advancements seen in Open LLMs. This work aims to address this gap by
conducting a comprehensive examination of the ability of agents to emulate
human personalities using Open LLMs. To achieve this, we generate a set of ten
LLM Agents based on the most representative Open models and subject them to a
series of assessments concerning the Myers-Briggs Type Indicator (MBTI) test.
Our approach involves evaluating the intrinsic personality traits of Open LLM
agents and determining the extent to which these agents can mimic human
personalities when conditioned by specific personalities and roles. Our
findings unveil that: $(i)$ each Open LLM agent showcases distinct human
personalities; $(ii)$ personality-conditioned prompting produces varying
effects on the agents, with only few successfully mirroring the imposed
personality, while most of them being ``closed-minded'' (i.e., they retain
their intrinsic traits); $(iii)$ combining role and personality conditioning
can enhance the agents' ability to mimic human personalities; and $(iv)$
personalities typically associated with the role of teacher tend to be emulated
with greater accuracy. Our work represents a step up in understanding the dense
relationship between NLP and human psychology through the lens of Open LLMs.

开放式大型语言模型（Open LLMs）的研究表明，模型代理人能够模拟人类个性特征，通过角色和个性的条件设置，能够更准确地模拟人类个性，尤其是与教师角色相关联的个性。

开放模型，封闭思维？关于开放大型语言模型模仿人类个性的能力

Open Models, Closed Minds? On Agents Capabilities in Mimicking Human  Personalities through Open Large Language Models

Reports of human-like behaviors in foundation models are growing, with
psychological theories providing enduring tools to investigate these behaviors.
However, current research tends to directly apply these human-oriented tools
without verifying the faithfulness of their outcomes. In this paper, we
introduce a framework, RealBehavior, which is designed to characterize the
humanoid behaviors of models faithfully. Beyond simply measuring behaviors, our
framework assesses the faithfulness of results based on reproducibility,
internal and external consistency, and generalizability. Our findings suggest
that a simple application of psychological tools cannot faithfully characterize
all human-like behaviors. Moreover, we discuss the impacts of aligning models
with human and social values, arguing for the necessity of diversifying
alignment objectives to prevent the creation of models with restricted
characteristics.

通过引入 RealBehavior 框架，本文旨在忠实地表征模型的人类行为，评估结果的忠实性，并讨论与人类和社会价值观的模型对齐目标多样化的必要性。

RealBehavior: 一个准确描述基础模型人类行为机制的框架

RealBehavior: A Framework for Faithfully Characterizing Foundation  Models' Human-like Behavior Mechanisms

Deep reinforcement learning (DRL) has attracted much attention in automated
game testing. Early attempts rely on game internal information for game space
exploration, thus requiring deep integration with games, which is inconvenient
for practical applications. In this work, we propose using only
screenshots/pixels as input for automated game testing and build a general game
testing agent, Inspector, that can be easily applied to different games without
deep integration with games. In addition to covering all game space for
testing, our agent tries to take human-like behaviors to interact with key
objects in a game, since some bugs usually happen in player-object
interactions. Inspector is based on purely pixel inputs and comprises three key
modules: game space explorer, key object detector, and human-like object
investigator. Game space explorer aims to explore the whole game space by using
a curiosity-based reward function with pixel inputs. Key object detector aims
to detect key objects in a game, based on a small number of labeled
screenshots. Human-like object investigator aims to mimic human behaviors for
investigating key objects via imitation learning. We conduct experiments on two
popular video games: Shooter Game and Action RPG Game. Experiment results
demonstrate the effectiveness of Inspector in exploring game space, detecting
key objects, and investigating objects. Moreover, Inspector successfully
discovers two potential bugs in those two games. The demo video of Inspector is
available at this https URL.

本研究提出了一种新的通用游戏测试代理 ——Inspector，该代理只使用屏幕截图 / 像素作为输入，通过使用基于好奇心的奖励函数，探索游戏空间，检测游戏中的关键对象，并通过模仿学习模拟人类行为来研究关键对象，实现了游戏空间的完整覆盖和探索，检测重要对象并按照人类行为相互作用，最终成功发现了两个游戏中的潜在漏洞。