Papers by Minmin Sun
FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)
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Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou
| Challenge: | Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU. |
| Approach: | They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models. |
| Outcome: | The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |