ShareLM集合与插件：为社区利益贡献人-模型对话

The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community

August 15, 2024

作者: Shachar Don-Yehiya, Leshem Choshen, Omri Abend

cs.AI

摘要

人机对话提供了一个窥视用户真实场景、行为和需求的窗口，因此对于模型开发和研究而言是宝贵的资源。尽管盈利公司通过其模型的API收集用户数据，将其用于内部改进自己的模型，但开源社区和研究界却落后于此。 我们介绍了ShareLM集合，这是一个包含与大型语言模型的人类对话的统一集合，以及其附带的插件，这是一个用于自愿贡献用户与模型对话的Web扩展。在少数平台分享他们的聊天记录的情况下，ShareLM插件增加了这一功能，从而允许用户从大多数平台分享对话。该插件允许用户对他们的对话进行评分，无论是在对话还是回复级别，并且在离开用户本地存储之前，用户可以删除他们希望保持私密的对话。我们将插件对话作为ShareLM集合的一部分发布，并呼吁在开放式人机数据领域加大社区努力。 代码、插件和数据均可获得。

English

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.