19 citations · 46 across the 6 of their papers we have counts for
8 papers
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks
Ziyi Liu, Tzu-Sheng Kuo, Jiale Liu +3
As large language models are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing diverse group dynamics across…
Understanding, Challenging, and Demystifying Perceptions of Gig Worker Vulnerabilities
Sander de Jong, Jane Hsieh, Tzu-Sheng Kuo +3
Across service domains, platform-based gig workers often face a wide range of severe yet hidden vulnerabilities, including opaque pay practices, illusions of flexibility, health an…
Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work
Jane Hsieh, Angie Zhang, Sajel Surati +6
The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are expos…
The Future of Open Human Feedback
Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo +17
Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desir…
PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation
Tzu-Sheng Kuo, Quan Ze Chen, Amy X. Zhang +3
Community and organizational policies are typically designed in a top-down, centralized fashion, with limited input from impacted stakeholders. This can result in policies that are…
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng +5
AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which…
Measuring Adversarial Datasets
Yuanchen Bai, Raoyi Huang, Vijay Viswanathan +2
In the era of widespread public use of AI systems across various domains, ensuring adversarial robustness has become increasingly vital to maintain safety and prevent undesirable e…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…