Papers by Yitao Yang
Adaptive Gating in Mixture-of-Experts based Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models employ a fixed gating network where each token is computed by the same number of experts. |
| Approach: | They propose a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution. |
| Outcome: | The proposed model reduces training time and inference quality while maintaining sparsity while maintaining inference accuracy. |
RouterHGC: Optimized Router for LLM-based Multi-Agent Systems via Heterogeneous Graph Contrastive Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs)-driven Multi-Agent Systems (MAS) have demonstrated remarkable scalability and generalizability across complex tasks. |
| Approach: | They propose a new framework for routing using large language models . they formalize routing as node selection through edge-weight prediction . |
| Outcome: | The proposed framework outperforms the best single LLM and baselines on five datasets . it achieves 0.80%–6.17% accuracy gains on MATH and HotpotQA while reducing inference cost by 27.40%. |
Quantification of Large Language Model Distillation (2025.acl-long)
Copied to clipboard
Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni
| Challenge: | Existing studies have revealed the robustness degra-dation caused by data distillation. |
| Approach: | They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization. |
| Outcome: | The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. |