Recent efforts have incorporated large language models (LLMs) with external
resources (e.g., the Internet) or internal control flows (e.g., prompt
chaining) for tasks requiring grounding or reasoning. However, these efforts
have largely been piecemeal, lacking a systematic framework for constructing a
fully-fledged language agent. To address this challenge, we draw on the rich
history of agent design in symbolic artificial intelligence to develop a
blueprint for a new wave of cognitive language agents. We first show that LLMs
have many of the same properties as production systems, and recent efforts to
improve their grounding or reasoning mirror the development of cognitive
architectures built around production systems. We then propose Cognitive
Architectures for Language Agents (CoALA), a conceptual framework to
systematize diverse methods for LLM-based reasoning, grounding, learning, and
decision making as instantiations of language agents in the framework. Finally,
we use the CoALA framework to highlight gaps and propose actionable directions
toward more capable language agents in the future.

利用符号人工智能的代理设计历史，我们提出了一种新的认知语言代理的蓝图，即 Cognitive Architectures for Language Agents (CoALA) 框架，这个框架将大型语言模型与外部资源或内部控制流结合起来，以实现基于语言模型的推理、概念化、学习和决策。通过 CoALA 框架，我们强调了目前语言代理的不足，并提出了未来发展更强大的语言代理的具体方向。

语言代理的认知架构

Cognitive Architectures for Language Agents

Few-shot NER needs to effectively capture information from limited instances
and transfer useful knowledge from external resources. In this paper, we
propose a self-describing mechanism for few-shot NER, which can effectively
leverage illustrative instances and precisely transfer knowledge from external
resources by describing both entity types and mentions using a universal
concept set. Specifically, we design Self-describing Networks (SDNet), a
Seq2Seq generation model which can universally describe mentions using
concepts, automatically map novel entity types to concepts, and adaptively
recognize entities on-demand. We pre-train SDNet with large-scale corpus, and
conduct experiments on 8 benchmarks from different domains. Experiments show
that SDNet achieves competitive performances on all benchmarks and achieves the
new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and
robustness.

本文提出了一种自我描述的机制，用于 few-shot NER，它可以通过使用通用概念集描述实体类型和提及，并从外部资源中精确地传输有用的知识；具有竞争性的结果，在各个基准测试上的表现证明了其有效性和鲁棒性。