In order to construct or extend entity-centric and event-centric knowledge
graphs (KG and EKG), the information extraction (IE) annotation toolkit is
essential. However, existing IE toolkits have several non-trivial problems,
such as not supporting multi-tasks, not supporting automatic updates. In this
work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for
KG and EKG construction. Specifically, for the multi-task issue, CollabKG
unifies different IE subtasks, including named entity recognition (NER),
entity-relation triple extraction (RE), and event extraction (EE), and supports
both KG and EKG. Then, combining advanced prompting-based IE technology, the
human-machine-cooperation mechanism with LLMs as the assistant machine is
presented which can provide a lower cost as well as a higher performance.
Lastly, owing to the two-way interaction between the human and machine,
CollabKG with learning ability allows self-renewal. Besides, CollabKG has
several appealing features (e.g., customization, training-free, propagation,
etc.) that make the system powerful, easy-to-use, and high-productivity. We
holistically compare our toolkit with other existing tools on these features.
Human evaluation quantitatively illustrates that CollabKG significantly
improves annotation quality, efficiency, and stability simultaneously.

CollabKG 是一种可学习的人机协同信息提取工具包，可用于构建或扩展以实体为中心和事件为中心的知识图谱，并结合先进的提示式信息提取技术，实现人机协同机制，提高了注释质量、效率和稳定性。

CollabKG：一种可学习的人机协作信息提取工具套件，用于 (事件) 知识图谱构建

CollabKG: A Learnable Human-Machine-Cooperative Information Extraction  Toolkit for (Event) Knowledge Graph Construction

Cooperation is often implicitly assumed when learning from other agents.
Cooperation implies that the agent selecting the data, and the agent learning
from the data, have the same goal, that the learner infer the intended
hypothesis. Recent models in human and machine learning have demonstrated the
possibility of cooperation. We seek foundational theoretical results for
cooperative inference by Bayesian agents through sequential data. We develop
novel approaches analyzing consistency, rate of convergence and stability of
Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the
effectiveness, sample efficiency and robustness show that cooperation is not
only possible in specific instances but theoretically well-founded in general.
We discuss implications for human-human and human-machine cooperation.

研究了合作推理的基础理论，提出了一种新方法 SCBI 来分析一致性，收敛率和稳定性，并表明合作对于特定情况不仅是可能的，而且从理论上讲在一般情况下也是可行的，以及探讨了人类之间和人机之间合作的意义。

顺序协作贝叶斯推理

Sequential Cooperative Bayesian Inference

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major
driving force behind technical progress has been competition with human
cognition. Historical milestones have been frequently associated with computers
matching or outperforming humans in difficult cognitive tasks (e.g. face
recognition [2], personality classification [3], driving cars [4], or playing
video games [5]), or defeating humans in strategic zero-sum encounters (e.g.
Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast,
less attention has been given to developing autonomous machines that establish
mutually cooperative relationships with people who may not share the machine's
preferences. A main challenge has been that human cooperation does not require
sheer computational power, but rather relies on intuition [11], cultural norms
[12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions
toward cooperation [17], common-sense mechanisms that are difficult to encode
in machines for arbitrary contexts. Here, we combine a state-of-the-art
machine-learning algorithm with novel mechanisms for generating and acting on
signals to produce a new learning algorithm that cooperates with people and
other machines at levels that rival human cooperation in a variety of
two-player repeated stochastic games. This is the first general-purpose
algorithm that is capable, given a description of a previously unseen game
environment, of learning to cooperate with people within short timescales in
scenarios previously unanticipated by algorithm designers. This is achieved
without complex opponent modeling or higher-order theories of mind, thus
showing that flexible, fast, and general human-machine cooperation is
computationally achievable using a non-trivial, but ultimately simple, set of
algorithmic mechanisms.

通过创新的信号生成和操作机制，结合最先进的机器学习算法，实现了计算机在人机互动博弈中与人类合作的能力，无需复杂的对手建模或心灵理论。