43 citations · 178 across the 39 of their papers we have counts for
4 papers · 1 filter
Concordia: Self-Improving Synthetic Tables for Federated LLMs
Jimin Huang, Duanyu Feng, Nuo Chen +8
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remai…
The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models
Yan Wang, Yitao Xu, Nanhan Shen +3
Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a pos…
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
Yuxin Wang, Duanyu Feng, Yongfu Dai +5
Data serves as the fundamental foundation for advancing deep learning, particularly tabular data presented in a structured format, which is highly conducive to modeling. However, e…
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
Duanyu Feng, Yongfu Dai, Jimin Huang +6
In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional cr…