OnGoal：在多轮对话中追踪与可视化大型语言模型的会话目标

摘要

隨著與大型語言模型（LLMs）的多輪對話變得越來越長且複雜，使用者如何能更好地評估和審查其對話目標的進展？我們提出了OnGoal，這是一個幫助使用者更好地管理目標進展的LLM聊天介面。OnGoal通過LLM輔助的評估提供即時的反饋，解釋評估結果並附上示例，以及展示目標隨時間的進展概覽，使使用者能夠更有效地導航複雜的對話。通過一項涉及20名參與者的寫作任務研究，我們將OnGoal與沒有目標追蹤的基準聊天介面進行了比較。使用OnGoal的參與者在探索新的提示策略以克服溝通障礙的同時，花費更少的時間和精力來達成目標，這表明追蹤和視覺化目標可以增強LLM對話中的參與度和韌性。我們的研究結果啟發了未來LLM聊天介面的設計方向，這些介面將改善目標溝通、降低認知負荷、增強互動性，並提供反饋以提升LLM的表現。

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型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

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