Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing
Not on the map. Why?
Abstract (arXiv)
We introduce Mephisto, a framework to make crowdsourcing for research more reproducible, transparent, and collaborative. Mephisto provides abstractions that cover a broad set of task designs and data collection workflows, and provides a simple user experience to make best-practices easy defaults. In this whitepaper we discuss the current state of data collection and annotation in ML research, establish the motivation for building a shared framework to enable researchers to create and open-source data collection and annotation tools as part of their publication, and outline a set of suggested requirements for a system to facilitate these goals. We then step through our resolution in Mephisto, explaining the abstractions we use, our design decisions around the user experience, and share implementation details and where they align with the original motivations. We also discuss current limitations, as well as future work towards continuing to deliver on the framework's initial goals. Mephisto is available as an open source project, and its documentation can be found at www.mephisto.ai.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 12 Jan 2023.
Builds on
15 of 32 references.
References · 15
- 2020Scaling Laws for Neural Language Modelscited by 6,586
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
- 2022Beyond neural scaling laws: beating power law scaling via data pruningcited by 528
- 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
- 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
- 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 171
- 2020Recipes for Safety in Open-domain Chatbotscited by 170
- 2017Deep Learning is Robust to Massive Label Noisecited by 165
- 2017ParlAI: A Dialog Research Software Platformcited by 139
- 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
Built on by
Citing papers per year: 2023: 5; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2023A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captionscited by 108
- 2023Multi-Party Chat: Conversational Agents in Group Settings with Humans and Modelscited by 54
- 2023Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversationscited by 4
- 2023Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systemscited by 1
- 2024BotEval: Facilitating Interactive Human Evaluationcited by 1
- 2026Doomed to Re-Annotate, Forever: The ImageNet Storycited by 1
- 2023Infusing Commonsense World Models with Graph Knowledgecited by 0
- 2026Doomed to Re-Annotate, Forever: The ImageNet Story13 Aug 2026
- 2024BotEval: Facilitating Interactive Human Evaluation25 Jul 2024
- 2023A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions14 Dec 2023
- 2023Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems26 Oct 2023
- 2023Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations30 Aug 2023
- 2023Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models26 Apr 2023
- 2023Infusing Commonsense World Models with Graph Knowledge13 Jan 2023