Control Flow Operators in PyTorch
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
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 forprofit 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 usermodel 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.
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks