Papers by Yuang Bian
PToco: Prefix-based Token-level Collaboration Enhances Reasoning for Multi-LLMs (2025.coling-main)
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| Challenge: | Existing approaches to collaboration between multiple Large Language Models (LLMs) rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer. |
| Approach: | They propose a mechanism that enables collaboration among less capable LLMs independent of tokenizer differences. |
| Outcome: | The proposed mechanism improves performance over individual models and generalizes well across different quantities and sizes of participating models. |
Enrich, Aggregate, and Generate: Three-stage Biomedical Data-to-Text Generation Using Large Language Models in Low-resource Scenarios (2026.findings-acl)
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| Challenge: | Biomedical data-to-text generation is a branch of Natural Language Generation, aiming at generating textual natural language descriptions that can fluently and precisely describe the structured data. |
| Approach: | They propose an LLM framework that can be used to generate textual natural language descriptions using in-context learning. |
| Outcome: | The proposed framework provides good interpretability and superior performance on the BioLeaflets dataset. |
LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)
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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan
| Challenge: | Existing LLMs are difficult to achieve satisfactory results in table-related tasks. |
| Approach: | They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks. |
| Outcome: | The proposed model achieves state-of-the-art on a Logic2Text dataset. |