ShareLM 数据集与插件：为社区利益贡献人机对话数据
The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community
AI总结：
本文提出 ShareLM 数据集及配套 Web 插件，允许用户自愿贡献并评分多平台人机对话，同时支持本地隐私删除，旨在促进开源社区人机对话数据的共享与研究。
摘要：
人机对话为了解用户的真实场景、行为和需求提供了窗口，因此是模型开发和研究的宝贵资源。尽管营利性公司通过其模型的 API 收集用户数据，并在内部使用它来改进自己的模型，但开源和研究社区却落后了。我们介绍了 ShareLM 数据集，这是一个统一的大语言模型人类对话集合，以及其配套插件，一个用于自愿贡献人机对话的 Web 扩展。在少数平台分享其聊天记录的情况下，ShareLM 插件添加了此功能，从而允许用户分享来自大多数平台的对话。该插件允许用户对他们的对话进行评分，无论是在对话级别还是响应级别，并且在他们选择保留隐私的对话离开用户本地存储之前将其删除。我们将插件对话作为 ShareLM 数据集的一部分发布，并呼吁在开放人机数据领域进行更多的社区努力。代码、插件和数据均已可用。
英文摘要：
Human-model conversations provide a window into users' real-world scenarios, behavior, and needs, and thus are a valuable resource for model development and research. While for-profit companies collect user data through the APIs of their models, using it internally to improve their own models, the open source and research community lags behind. We introduce the ShareLM collection, a unified set of human conversations with large language models, and its accompanying plugin, a Web extension for voluntarily contributing user-model conversations. Where few platforms share their chats, the ShareLM plugin adds this functionality, thus, allowing users to share conversations from most platforms. The plugin allows the user to rate their conversations, both at the conversation and the response levels, and delete conversations they prefer to keep private before they ever leave the user's local storage. We release the plugin conversations as part of the ShareLM collection, and call for more community effort in the field of open human-model data. The code, plugin, and data are available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
