OnGoal：在多轮对话中追踪与可视化大语言模型的会话目标

摘要

随着与大型语言模型（LLMs）的多轮对话日益延长且复杂，用户如何更好地评估和回顾其对话目标的进展？我们推出了OnGoal，一款旨在帮助用户更有效管理目标进展的LLM聊天界面。OnGoal通过LLM辅助评估提供实时的目标对齐反馈，结合实例解释评估结果，并展示目标随时间推移的概览，从而使用户能够更高效地驾驭复杂对话。在一项涉及20名参与者的写作任务研究中，我们将OnGoal与无目标追踪的基础聊天界面进行了对比评估。使用OnGoal的参与者在达成目标时花费的时间和精力更少，同时探索新的提示策略以克服沟通障碍，这表明目标追踪与可视化能够增强LLM对话中的参与度和韧性。我们的研究结果为未来LLM聊天界面的设计提供了启示，旨在改善目标传达、减轻认知负荷、增强交互性，并通过反馈提升LLM性能。

English

As multi-turn dialogues with large language models (LLMs) grow longer and more complex, how can users better evaluate and review progress on their conversational goals? We present OnGoal, an LLM chat interface that helps users better manage goal progress. OnGoal provides real-time feedback on goal alignment through LLM-assisted evaluation, explanations for evaluation results with examples, and overviews of goal progression over time, enabling users to navigate complex dialogues more effectively. Through a study with 20 participants on a writing task, we evaluate OnGoal against a baseline chat interface without goal tracking. Using OnGoal, participants spent less time and effort to achieve their goals while exploring new prompting strategies to overcome miscommunication, suggesting tracking and visualizing goals can enhance engagement and resilience in LLM dialogues. Our findings inspired design implications for future LLM chat interfaces that improve goal communication, reduce cognitive load, enhance interactivity, and enable feedback to improve LLM performance.

OnGoal：在多轮对话中追踪与可视化大语言模型的会话目标

OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models

摘要

Support