梳理目标导向交互智能体的复杂任务与评估环境。
A Survey on Complex Tasks for Goal-Directed Interactive Agents
- 按挑战维度整理智能体任务与环境
- 涵盖大语言模型推动的新挑战任务
- 适合研究智能体评估与任务设计者
目标导向的交互智能体可通过与环境互动自主完成任务，助力人类日常生活。近期大型语言模型（LLMs）的发展催生了越来越多更复杂的评估任务。为准确理解这些任务对智能体带来的不同挑战，本综述系统整理了相关任务与环境，并按关键维度进行结构化归纳。资源更新可访问项目网站：https://coli-saar.github.io/interactive-agents。
原文摘要 · Abstract (English)
Goal-directed interactive agents, which autonomously complete tasks through interactions with their environment, can assist humans in various domains of their daily lives. Recent advances in large language models (LLMs) led to a surge of new, more and more challenging tasks to evaluate such agents. To properly contextualize performance across these tasks, it is imperative to understand the different challenges they pose to agents. To this end, this survey compiles relevant tasks and environments for evaluating goal-directed interactive agents, structuring them along dimensions relevant for understanding current obstacles. An up-to-date compilation of relevant resources can be found on our project website: https://coli-saar.github.io/interactive-agents.
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