Zian Zheng — accepted AI-conference papers

2025

MixEval-X: Any-to-any Evaluations from Real-world Data Mixture

Jinjie Ni, Yifan Song, Deepanway Ghosal, Bo Li, David Junhao Zhang, Xiang Yue +8

ICLR 2025spotlight

Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) inconsistent standards, shaped by different communit…

Cited by 3 Source ↗PDF ↗Scholar ↗

2024

OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Fuzhao Xue, Zian Zheng, Yao Fu, Jinjie Ni, Zangwei Zheng, Wangchunshu Zhou +1

ICML 2024poster

To help the open-source community have a better understanding of Mixture-of-Experts (MoE) based large language models (LLMs), we train and release OpenMoE, a series of fully open-sourced and reproducible decoder-only MoE LLMs, ranging from 650M to 34B parameters and trained on up to over 1T tokens.…

Cited by 84 Source ↗PDF ↗code ↗Scholar ↗