Current language model-driven agents often lack mechanisms for effective user
participation, which is crucial given the vagueness commonly found in user
instructions. Although adept at devising strategies and performing tasks, these
agents struggle with seeking clarification and grasping precise user
intentions. To bridge this gap, we introduce Intention-in-Interaction (IN3), a
novel benchmark designed to inspect users' implicit intentions through explicit
queries. Next, we propose the incorporation of model experts as the upstream in
agent designs to enhance user-agent interaction. Employing IN3, we empirically
train Mistral-Interact, a powerful model that proactively assesses task
vagueness, inquires user intentions, and refines them into actionable goals
before starting downstream agent task execution. Integrating it into the XAgent
framework, we comprehensively evaluate the enhanced agent system regarding user
instruction understanding and execution, revealing that our approach notably
excels at identifying vague user tasks, recovering and summarizing critical
missing information, setting precise and necessary agent execution goals, and
minimizing redundant tool usage, thus boosting overall efficiency. All the data
and codes are released.

介绍了一种新的基准测试 Intention-in-Interaction (IN3)，用于通过明确的查询检查用户的隐含意图，提出将模型专家纳入代理设计以增强用户 - 代理之间的交互，通过 IN3 实证训练了一种强大模型 Mistral-Interact，其主动评估任务的模糊性，询问用户意图，并在启动下游代理任务执行之前对其进行细化，通过将其集成到 XAgent 框架中，全面评估了增强代理系统在用户指示理解和执行方面的性能，表明该方法在识别模糊用户任务、恢复和总结关键缺失信息、设定精确和必要的代理执行目标，以及减少冗余工具使用方面明显优于其他方法，从而提高了整体效率。所有数据和代码均已发布。

告诉我更多！朝着基于语言模型的代理程序的隐式用户意图理解

Tell Me More! Towards Implicit User Intention Understanding of Language  Model Driven Agents

User contributions in the form of posts, comments, and votes are essential to
the success of online communities. However, allowing user participation also
invites undesirable behavior such as trolling. In this paper, we characterize
antisocial behavior in three large online discussion communities by analyzing
users who were banned from these communities. We find that such users tend to
concentrate their efforts in a small number of threads, are more likely to post
irrelevantly, and are more successful at garnering responses from other users.
Studying the evolution of these users from the moment they join a community up
to when they get banned, we find that not only do they write worse than other
users over time, but they also become increasingly less tolerated by the
community. Further, we discover that antisocial behavior is exacerbated when
community feedback is overly harsh. Our analysis also reveals distinct groups
of users with different levels of antisocial behavior that can change over
time. We use these insights to identify antisocial users early on, a task of
high practical importance to community maintainers.

本研究通过对三个大型在线讨论社区被封禁用户的分析，表征了不良行为。我们发现这些用户更容易发表与主题无关的内容，并更容易引起其他用户的反应，而实施不良行为通常发生在社区反馈过度苛刻的情况下。此外，我们还发现这些用户的排斥度随着时间的推移而增加，更易被社区禁言。 研究结果表明，在线社区维护者会从这些信息中获得帮助，从而更早地发现和限制不受欢迎的行为。