Papers by Yunkai Zhang
TARo: Token-level Adaptive Routing for LLM Test-time Alignment (2026.findings-acl)
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Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance. |
| Approach: | They propose to use token-level Adaptive Routing to steer frozen LLMs toward structured reasoning entirely at inference time. |
| Outcome: | Extensive experiments show that TARo significantly improves reasoning performance by up to +22.4% over base model and +8.4% . |
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)
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| Challenge: | Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema. |
| Approach: | They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data. |
| Outcome: | The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity. |