让大模型对话目标可视化，帮助用户更高效达成目标。
OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models
- 用大模型实时评估对话是否符合用户目标
- 提供目标进展概览和解释，减少认知负担
- 适合需要长期对话的写作、规划类任务
随着大语言模型（LLMs）的多轮对话日益复杂，用户如何有效评估和回顾对话目标的进展？我们提出OnGoal，一个支持目标追踪与可视化的LLM聊天界面。它通过大模型辅助评估提供实时目标对齐反馈，结合示例解释评估结果，并展示目标随时间的推进情况，使用户能更高效地管理复杂对话。在20名参与者参与的写作任务研究中，使用OnGoal的用户比使用无目标追踪基线界面的用户花费更少时间和精力达成目标，且更主动探索新提示策略以克服误解。结果表明，目标追踪与可视化可提升对话中的参与度与韧性。研究为未来LLM聊天界面的设计提供了启示：改善目标沟通、降低认知负荷、增强交互性，并支持反馈以优化大模型表现。
原文摘要 · Abstract (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.
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