Individuality and personalization comprise the distinctive characteristics
that make each writer unique and influence their words in order to effectively
engage readers while conveying authenticity. However, our growing reliance on
LLM-based writing assistants risks compromising our creativity and
individuality over time. We often overlook the negative impacts of this trend
on our creativity and uniqueness, despite the possible consequences. This study
investigates these concerns by performing a brief survey to explore different
perspectives and concepts, as well as trying to understand people's viewpoints,
in conjunction with past studies in the area. Addressing these issues is
essential for improving human-computer interaction systems and enhancing
writing assistants for personalization and individuality.

个性化与个性化组成了使每个作者独特的特征，影响他们的文字以有效地吸引读者并传达真实性。然而，我们对基于 LLM 的写作助手的依赖日益增长，却可能在时间上妥协了我们的创造力和个性。本研究通过进行简要调查，以探索不同观点和概念，并尝试理解人们的观点，结合在该领域的过去研究，来研究这些问题。解决这些问题对于改进人机交互系统和提高个性化与个人特有的写作助手至关重要。

墨水与个性：在 LLM 时代打造个性化叙事

Ink and Individuality: Crafting a Personalised Narrative in the Age of  LLMs

Several approaches have been presented, which aim to extract models from
natural language specifications. These approaches have inherent weaknesses for
they assume an initial problem understanding that is perfect, and they leave no
room for feedback. Motivated by real-world collaboration settings between
requirements engineers and customers, this paper proposes an interaction
blueprint that aims for dialogue based, computer aided software requirements
analysis. Compared to mere model extraction approaches, this interaction
blueprint encourages individuality, creativity and genuine compromise. A
simplistic Experiment was conducted to showcase the general idea. This paper
discusses the experiment as well as the proposed interaction blueprint and
argues, that advancements in natural language processing and generative AI
might lead to significant progress in a foreseeable future. However, for that,
there is a need to move away from a magical black box expectation and instead
moving towards a dialogue based approach that recognizes the individuality that
is an undeniable part of requirements engineering.

借鉴实际需求工程师与客户之间的协作模式，该论文提出了一种基于对话的计算机辅助软件需求分析的交互蓝图。与纯粹的模型提取方法相比，该交互蓝图鼓励个性、创造力和真实的妥协。通过一个简单的实验证明了这一总体理念，并认为自然语言处理和生成型人工智能的进展可能在可预见的未来取得重大进展，然而，这需要从神奇的黑盒子期望中远离，并转向承认作为需求工程的不可否认的个性的对话式方法。

面向对话式的计算机辅助软件需求获取

Towards dialogue based, computer aided software requirements elicitation

Individuality is essential in human society, which induces the division of
labor and thus improves the efficiency and productivity. Similarly, it should
also be the key to multi-agent cooperation. Inspired by that individuality is
of being an individual separate from others, we propose a simple yet efficient
method for the emergence of individuality (EOI) in multi-agent reinforcement
learning (MARL). EOI learns a probabilistic classifier that predicts a
probability distribution over agents given their observation and gives each
agent an intrinsic reward of being correctly predicted by the classifier. The
intrinsic reward encourages the agents to visit their own familiar
observations, and learning the classifier by such observations makes the
intrinsic reward signals stronger and the agents more identifiable. To further
enhance the intrinsic reward and promote the emergence of individuality, two
regularizers are proposed to increase the discriminability of the classifier.
We implement EOI on top of popular MARL algorithms. Empirically, we show that
EOI significantly outperforms existing methods in a variety of multi-agent
cooperative scenarios.

本文提出了一种基于强化学习的多智体合作算法，特别着重于个体化。这种算法学习一个概率分类器来预测代理人身份，从而赋予每个代理人固有的动机，鼓励代理人访问自己熟悉的观测，促进个体性的出现，并在多种多智体协同情景中显著优于现有方法。