The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings
new requirements and opportunities to current sensing approaches, especially in
light of recent progress in Chatbots and Large Language Models (LLMs).
Therefore, the advancement of parallel intelligence-powered Crowdsensing
Intelligence (CSI) is witnessed, which is currently advancing towards
linguistic intelligence. In this paper, we propose a novel sensing paradigm,
namely conversational crowdsensing, for Industry 5.0. It can alleviate workload
and professional requirements of individuals and promote the organization and
operation of diverse workforce, thereby facilitating faster response and wider
popularization of crowdsensing systems. Specifically, we design the
architecture of conversational crowdsensing to effectively organize three types
of participants (biological, robotic, and digital) from diverse communities.
Through three levels of effective conversation (i.e., inter-human, human-AI,
and inter-AI), complex interactions and service functionalities of different
workers can be achieved to accomplish various tasks across three sensing phases
(i.e., requesting, scheduling, and executing). Moreover, we explore the
foundational technologies for realizing conversational crowdsensing,
encompassing LLM-based multi-agent systems, scenarios engineering and
conversational human-AI cooperation. Finally, we present potential industrial
applications of conversational crowdsensing and discuss its implications. We
envision that conversations in natural language will become the primary
communication channel during crowdsensing process, enabling richer information
exchange and cooperative problem-solving among humans, robots, and AI.

本文提出了一种新的感知范式，名为对话式众包感知，致力于解决 Industry 5.0 中的工作负荷、职业要求和多样化劳动力组织与运作等问题，以此促进众包感知系统的更快响应和更广泛普及。该范式通过有效组织不同社区的三种类型参与者（生物、机器人和数字）展开复杂交互和服务功能，实现三个感知阶段的各种任务的完成，并探索实现对话式众包感知的基础技术。同时，讨论了对话式众包感知的潜在工业应用和影响。我们设想在众包感知过程中，自然语言对话将成为主要的沟通渠道，使人类、机器人和人工智能之间实现更丰富的信息交流和合作解决问题。

会话式众感知：并行智能驱动的新型感知方法

Conversational Crowdsensing: A Parallel Intelligence Powered Novel  Sensing Approach

We introduce a lifelong language learning setup where a model needs to learn
from a stream of text examples without any dataset identifier. We propose an
episodic memory model that performs sparse experience replay and local
adaptation to mitigate catastrophic forgetting in this setup. Experiments on
text classification and question answering demonstrate the complementary
benefits of sparse experience replay and local adaptation to allow the model to
continuously learn from new datasets. We also show that the space complexity of
the episodic memory module can be reduced significantly (~50-90%) by randomly
choosing which examples to store in memory with a minimal decrease in
performance. We consider an episodic memory component as a crucial building
block of general linguistic intelligence and see our model as a first step in
that direction.

提出一种生命化语言学习的模型，其中采用了 “稀疏经验回放” 和 “本地适应” 以减轻灾难性遗忘，并将这种情况应用于文本分类和问题回答中，证明了这种模型的优越性，同时表明通过随机选择存储在内存中的样本可以大大减少经验记忆模块的空间复杂度，并认为经验记忆组件是构建通用语言智能的重要组成部分。

终身语言学习中的情景记忆

Episodic Memory in Lifelong Language Learning

We define general linguistic intelligence as the ability to reuse previously
acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic
conventions to adapt to new tasks quickly. Using this definition, we analyze
state-of-the-art natural language understanding models and conduct an extensive
empirical investigation to evaluate them against these criteria through a
series of experiments that assess the task-independence of the knowledge being
acquired by the learning process. In addition to task performance, we propose a
new evaluation metric based on an online encoding of the test data that
quantifies how quickly an existing agent (model) learns a new task. Our results
show that while the field has made impressive progress in terms of model
architectures that generalize to many tasks, these models still require a lot
of in-domain training examples (e.g., for fine tuning, training task-specific
modules), and are prone to catastrophic forgetting. Moreover, we find that far
from solving general tasks (e.g., document question answering), our models are
overfitting to the quirks of particular datasets (e.g., SQuAD). We discuss
missing components and conjecture on how to make progress toward general
linguistic intelligence.

我们定义语言智能为利用先前获得的语言词汇、句法、语义和语用约定知识的能力，以便快速适应新任务。通过一系列实验来评估最先进的自然语言理解模型，以此定义我们的标准，并提出了一种新的评估方法，基于测试数据的在线编码，衡量现有代理（模型）学习新任务的速度。我们的研究发现，虽然现有模型在许多任务上具有广泛的推广能力，但仍需要大量域内训练示例，并且容易发生灾难性遗忘。此外，我们发现，我们的模型远未解决一般任务（例如文档问答），而是过度拟合了特定数据集的怪癖。最后，我们讨论了缺失的部分，并猜测如何在通向一般语言智能的进程中取得进展。