On social networks, algorithmic personalization drives users into filter
bubbles where they rarely see content that deviates from their interests. We
present a model for content curation and personalization that avoids filter
bubbles, along with algorithmic guarantees and nearly matching lower bounds. In
our model, the platform interacts with $n$ users over $T$ timesteps, choosing
content for each user from $k$ categories. The platform receives stochastic
rewards as in a multi-arm bandit. To avoid filter bubbles, we draw on the
intuition that if some users are shown some category of content, then all users
should see at least a small amount of that content. We first analyze a naive
formalization of this intuition and show it has unintended consequences: it
leads to ``tyranny of the majority'' with the burden of diversification borne
disproportionately by those with minority interests. This leads us to our model
which distributes this burden more equitably. We require that the probability
any user is shown a particular type of content is at least $\gamma$ times the
average probability all users are shown that type of content. Full
personalization corresponds to $\gamma = 0$ and complete homogenization
corresponds to $\gamma = 1$; hence, $\gamma$ encodes a hard cap on the level of
personalization. We also analyze additional formulations where the platform can
exceed its cap but pays a penalty proportional to its constraint violation. We
provide algorithmic guarantees for optimizing recommendations subject to these
constraints. These include nearly matching upper and lower bounds for the
entire range of $\gamma \in [0,1]$ showing that the reward of a multi-agent
variant of UCB is nearly optimal. Using real-world preference data, we
empirically verify that under our model, users share the burden of
diversification with only minor utility loss under our constraints.

该研究提出了一种可避免过滤气泡的内容筛选和个性化模型，该模型采用多臂赌博机策略，并提供了算法保证和匹配下限。为了避免过滤气泡，该作者集中在由 $i$ 用户在 $T$ 时间步内与平台互动选择来自 $k$ 个类别的内容，同时该平台根据多臂赌博机的概率同时展示少量的不同内容。最后我们提出了一个新型模型，该模型更公平地分配了举例分散的负担，可对推荐进行优化。

社交网络中减少极端化倾向

Disincentivizing Polarization in Social Networks

Large Language Models (LLMs) have demonstrated impressive text generation
capabilities, prompting us to reconsider the future of human-AI co-creation and
how humans interact with LLMs. In this paper, we present a spectrum of content
generation tasks and their corresponding human-AI interaction patterns. These
tasks include: 1) fixed-scope content curation tasks with minimal human-AI
interactions, 2) independent creative tasks with precise human-AI interactions,
and 3) complex and interdependent creative tasks with iterative human-AI
interactions. We encourage the generative AI and HCI research communities to
focus on the more complex and interdependent tasks, which require greater
levels of human involvement.

本文探讨了大型语言模型在文本生成方面的出色表现，介绍了不同人工智能与人类交互模式下的文本生成任务谱系，并鼓励研究人员关注需要更高程度人类参与的复杂、相互依存的创作任务。

人工智能与人类合作文本创作中的交互设计空间

Mapping the Design Space of Interactions in Human-AI Text Co-creation Tasks

Legal technology is currently receiving a lot of attention from various
angles. In this contribution we describe the main technical components of a
system that is currently under development in the European innovation project
Lynx, which includes partners from industry and research. The key contribution
of this paper is a workflow manager that enables the flexible orchestration of
workflows based on a portfolio of Natural Language Processing and Content
Curation services as well as a Multilingual Legal Knowledge Graph that contains
semantic information and meaningful references to legal documents. We also
describe different use cases with which we experiment and develop prototypical
solutions.

这篇论文介绍了欧洲创新项目 Lynx 中正在开发的一个系统的技术组件，包括工作流程管理器，基于自然语言处理和内容管理服务的工作流程的灵活编排，以及包含语义信息和有意义的法律文件参考的多语言法律知识图。论文还描述了不同的使用案例以及我们正在实验和开发的原型解决方案。