565 citations · 567 across the 6 of their papers we have counts for
6 papers
EMO: Frustratingly Easy Progressive Training of Extendable MoE
Linghao Jin, Chufan Shi, Huijuan Wang +4
Sparse Mixture-of-Experts (MoE) models offer a powerful way to scale model size without increasing compute, as per-token FLOPs depend only on k active experts rather than the total…
Handle with CARE: Can LLMs Reproduce How Online Communities React?
Nuan Wen, Xuezhe Ma, Chanbin Lim
Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick descriptions" (Geertz, 1973) of human commun…
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
Zhivar Sourati, Meltem Ozcan, Colin McDaniel +5
Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emoti…
Can Language Model Moderators Improve the Health of Online Discourse?
Hyundong Cho, Shuai Liu, Taiwei Shi +8
Conversational moderation of online communities is crucial to maintaining civility for a constructive environment, but it is challenging to scale and harmful to moderators. The inc…
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
Mingyu Derek Ma, Alexander K. Taylor, Nuan Wen +9
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by com…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…