Papers by Xiangyu Lin
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)
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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP. |
| Approach: | They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens. |
| Outcome: | The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. |
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)
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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian
| Challenge: | Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions. |
| Approach: | They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements. |
| Outcome: | Experiments on two temporal QA benchmarks show the proposed method works. |
LongAttn: Selecting Long-context Training Data via Token-level Attention (2025.findings-acl)
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| Challenge: | Existing methods to select long-context data often rely on sentence-level analysis, which can be greatly optimized in both performance and efficiency. |
| Approach: | They propose a token-level framework which quantifies long-range dependencies for LLMs by calculating token-based dependency strength and distribution uniformity of token scores. |
| Outcome: | The proposed framework quantifies long-range dependencies, enabling more accurate and efficient data selection. |
Can LLMs Hear the Dogwhistle? (2026.findings-acl)
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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei
| Challenge: | Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles. |
| Approach: | They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices . |
| Outcome: | The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts. |
Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing approaches to reducing wrongly labeled instances are based on a bag-level setting . however, sentence-level training is vulnerable to the noise brought by DS, which limits its application. |
| Approach: | They propose a transitive instance weighting mechanism integrated with the self-distilled BERT backbone to generate dynamic instance weights for denoised sentence-level training. |
| Outcome: | The proposed method can tackle wrongly labeled instances and prevent overfitting. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning (2021.emnlp-main)
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| Challenge: | Distantly supervised relation extraction is used in knowledge bases but its low quality and noisy sentences are present in sentence bags. |
| Approach: | They propose a multi-layer revision network which emphasizes inner-sentence correlations before extracting relevant information within sentences. |
| Outcome: | The proposed method improves on two New York Times datasets. |
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)
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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian
| Challenge: | Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases. |
| Approach: | They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction. |
| Outcome: | The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |