Papers by Yupian Lin
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. |
DoTAT: A Domain-oriented Text Annotation Tool (2022.acl-demo)
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| Challenge: | DoTAT is a domain-oriented text annotation tool that can reduce the time for event annotation by 19.7% . the tool supports multi-person collaborative process with automatically merging and review . |
| Approach: | They propose a domain-oriented text annotation tool called DoTAT . it provides multi-person collaborative process with automatic merging and review . |
| Outcome: | The proposed tool can reduce the time for event annotation by 19.7% compared with existing tools. |
CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation (2025.findings-acl)
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Guangya Yu, Yanhao Li, Zongying Jiang, Yuxiong Jin, Li Dai, Yupian Lin, Ruihui Hou, Weiyan Zhang, Yongqi Fan, Qi Ye, Jingping Liu, Tong Ruan
| Challenge: | Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. |
| Approach: | They propose a Chinese electronic medical records-based dataset for MQCIC and propose CF-IR method that disentangles clinical fact verification and inferential rule reasoning actions. |
| Outcome: | The proposed method outperforms Chain-of-Thought methods on 20 representative LLMs, covering general and medical models. |
Text-to-ES Bench: A Comprehensive Benchmark for Converting Natural Language to Elasticsearch Query (2025.acl-long)
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DonggeXue DonggeXue, Zhili Pu, Zhentao Xia, Hongli Sun, Ruihui Hou, Guangya Yu, Yupian Lin, Yongqi Fan, Jingping Liu, Tong Ruan
| Challenge: | Recent research on text-to-Query has explored using large language models to convert user query intent to executable code. |
| Approach: | They propose a novel semantic parsing task that leverages large language models to generate domain-specific language and post-processing code to support multi-index Elasticsearch queries. |
| Outcome: | The proposed model outperforms DeepSeek-R1 on the large Elasticsearch Dataset (LED) and BirdES datasets. |
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. |