The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 15 Aug 2024.
Builds on
13 of 24 references.
References · 13
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
- 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
- 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
- 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
- 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
- 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
- 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 397
- 2015Towards AI-Complete Question Answering: A Set of Prerequisite Toy Taskscited by 314
- 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 144
- 2019Learning from Dialogue after Deployment: Feed Yourself, Chatbot!cited by 102
1 more
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Citing papers per year: 2024: 1; 2025: 1; 2026: 6.
8 papers.
- 2024WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wildcited by 20
- 2025ShareChat: A Dataset of Chatbot Conversations in the Wildcited by 15
- 2026Do LLMs Benefit From Their Own Words?cited by 5
- 2026AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?cited by 5
- 2026The Culture Funnel: You Can't Align What isn't in the Datacited by 2
- 2026Measuring Distribution Shift in User Prompts and Its Effects on LLM Performancecited by 1
- 2026FineInstructions: Scaling Synthetic Instructions to Pre-Training Scalecited by 0
- 2026User Feedback Provides a Unique Signal that LLMs Can not Detectcited by 0
- 2026User Feedback Provides a Unique Signal that LLMs Can not Detect2 Sep 2026
- 2026The Culture Funnel: You Can't Align What isn't in the Data11 Jun 2026
- 2026Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance19 Apr 2026
- 2026AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?20 Mar 2026
- 2026Do LLMs Benefit From Their Own Words?27 Feb 2026
- 2026FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale29 Jan 2026
- 2025ShareChat: A Dataset of Chatbot Conversations in the Wild19 Dec 2025
- 2024WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild5 Sep 2024