The recent advent of large language models (LLM) has resulted in high-performing conversational agents such as chatGPT. These agents must remember key information from an ongoing conversation to provide responses that are contextually relevant to the user. However, these agents have limited memory and can be distracted by irrelevant parts of the conversation. While many strategies exist to manage conversational memory, users currently lack affordances for viewing and controlling what the agent remembers, resulting in a poor mental model and conversational breakdowns. In this paper, we present Memory Sandbox, an interactive system and design probe that allows users to manage the conversational memory of LLM-powered agents. By treating memories as data objects that can be viewed, manipulated, recorded, summarized, and shared across conversations, Memory Sandbox provides interaction affordances for users to manage how the agent should `see' the conversation.

翻译：大型语言模型（LLM）的最新进展催生了如ChatGPT等高性能对话代理。这些代理需持续记录对话中的关键信息，以向用户提供上下文相关的回复。然而，这些代理的记忆容量有限，且易受对话中无关信息的干扰。尽管现有多种对话记忆管理策略，用户目前仍缺乏查看和控制代理记忆内容的手段，导致认知模型缺失与对话中断。本文提出"记忆沙盒"——一个支持用户管理LLM驱动代理对话记忆的交互式系统与设计探针。通过将记忆转化为可跨对话查看、操作、记录、摘要与共享的数据对象，记忆沙盒为用户提供了管理代理如何"理解"对话的交互手段。