Papers by Songhao Wu
Union-of-Experts: Neurons in Mixture-of-Experts are Secretly Routers (2026.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts, resulting in suboptimal performance. |
| Approach: | They propose an MoE variant that performs "expert-autonomous routing" by pre-designating a fraction of neurons within each expert as "routing neurons" they pre-train UoE models with up to 3B parameters and show they outperform traditional MoEs with matched efficiency. |
| Outcome: | The proposed model outperforms existing models with 3B parameters and provides valuable insights into expert-autonomous selection and the broader routing mechanisms of MoE models. |
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment (2026.acl-long)
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| Challenge: | Current approaches to align large language models assume uniform human preferences, overlooking the diversity inherent in human populations. |
| Approach: | They propose a framework for scalable personalized alignment of large language models . they establish a preference space characterizing psychological and behavioral dimensions . |
| Outcome: | The proposed framework improves on existing methods with an average of 17.06% accuracy gain across four benchmarks and a strong adaptation capability to novel preferences. |