Papers by Yefeng Zheng

Can LLMs Replace Clinical Doctors? Exploring Bias in Disease Diagnosis by Large Language Models (2024.findings-emnlp)

Challenge:	Existing knowledge editing approaches struggle with sequential editing scenarios and harm the general capabilities of the model.
Approach:	They propose a framework that combines robust supervised fine-tuning and model merging for knowledge editing to combine supervised and supervised learning.
Outcome:	The proposed approach outperforms existing methods in sequential editing while preserving the original performance of the model.

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Yutian Zhao, Huimin Wang, Yuqi Liu, Wu Suhuang, Xian Wu, Yefeng Zheng

Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models (2025.findings-acl)

Challenge:	a new study examines the bias of disease prediction in large language models . the model biases are prevalent across gender, age range and disease judgment behaviors .
Approach:	They propose a prompt-based approach to alleviate the bias in disease prediction with LLMs.
Outcome:	The proposed model alleviates the observed bias in disease prediction with LLMs.

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Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, QingqingLong QingqingLong, Yefeng Zheng, Xian Wu

A Layered Debating Multi-Agent System for Similar Disease Diagnosis (2025.naacl-short)

Challenge:	Hallucination is a critical challenge for large language models and large vision-language models (LVLMs) however, dedicated research on medical hallucinations remains unexplored.
Approach:	They provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes.
Outcome:	The proposed models have demonstrated impressive performance on a variety of medical benchmarks.

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Yutian Zhao, Huimin Wang, Yefeng Zheng, Xian Wu

Challenge:	Traditional classification, contrastive learning, and large language models fail to detect subtle clues necessary for differentiation.
Approach:	They propose a framework that leverages Large Language Models to achieve accurate disease diagnosis . they structure patient information and integrate extensive medical knowledge to guide the analysis .
Outcome:	The proposed framework aims to identify subtle differences between similar diseases . the proposed framework can be used in clinical practice to improve accuracy .

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting (2021.acl-long)

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Yi Cheng, Siyao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

Text Generation

Challenge:	Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones.
Approach:	They propose a framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.
Outcome:	The proposed framework increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.

Knowledge-aware Attention Network for Medication Effectiveness Prediction (2024.lrec-main)

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Yingying Zhang, Xian Wu, Yu Zhang, Yefeng Zheng

Knowledge Representation

Challenge:	Existing effectiveness prediction methods focus on one specific medicine, one specific disease, or one specific lab test, making it hard to extend to general medicines and diseases in hospital/ICU scenarios.
Approach:	They propose to use knowledge enhanced module to incorporate external knowledge about medications and a medical feature learning module to determine the interaction between diagnosis and medications.
Outcome:	The proposed model outperforms state-of-the-art methods on a public dataset showing that it significantly outperformed existing models.

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)

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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

Challenge:	Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context.
Approach:	They propose a benchmark to evaluate how large vision language models understand memes in their original context.
Outcome:	The proposed benchmark evaluates how large vision language models understand meme intent in their original context.

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Information Extraction & Text Mining

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.

Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval (2021.acl-long)

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Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng

Information Retrieval

Challenge:	Existing methods for document hashing combine only one of semantics and neighborhood information, lacking a theoretical principle to guide the integration process.
Approach:	They propose to encode neighborhood information with a graph-induced Gaussian distribution and integrate it with generative models.
Outcome:	The proposed model can be trained as efficiently as state-of-the-art methods on benchmark datasets.

An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)

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Ziheng Zhang, Hualuo Liu, Jiaoyan Chen, Xi Chen, Bo Liu, YueJia Xiang, Yefeng Zheng

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding (2021.findings-acl)

Challenge:	Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines.
Approach:	They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context.
Outcome:	The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks.

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Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng Zheng

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)

Challenge:	Existing methods for aligning knowledge graph entities ignore the ontology which contains critical meta information such as classes and membership relationships with entities.
Approach:	They propose an ontology-guided method where KGs and ontologies are jointly embedded.
Outcome:	Extensive experiments on seven public and industrial benchmarks show the ontology-guided method performs well and is cost-effective.

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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng

Information Extraction & Text Mining

Challenge:	Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency.
Approach:	They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment .
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples.

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management (2021.naacl-main)

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Zhengxu Hou, Bang Liu, Ruihui Zhao, Zijing Ou, Yafei Liu, Xi Chen, Yefeng Zheng

Dialogue Systems & Conversational Agents

Challenge:	Existing research on taskoriented dialog systems mainly includes pipeline and end-to-end methods due to its non-differentiable nature.
Approach:	They propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot.
Outcome:	The proposed approach significantly improves performance and speed of training in a wide range of dialog systems.

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

imapScore: Medical Fact Evaluation Made Easy (2024.findings-acl)

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.

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Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning (2026.findings-acl)

Challenge:	Automated evaluation of natural language generation tasks fails to focus on medical QA because of the diversity in medical terminology.
Approach:	They propose a new data structure, imap, to capture key information in questions and answers.
Outcome:	The proposed model outperforms state-of-the-art metrics in correlation with human scores.

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Zichuan Fu, Xian Wu, Guojing Li, Yejing Wang, Yijun Chen, Zhao Zihao, Luo Yixuan, Hanyu Yan, Yefeng Zheng, Xiangyu Zhao

RareSyn: Health Record Synthesis for Rare Disease Diagnosis (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have catalyzed the rise of reasoningintensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers.
Approach:	They propose a large-small LLM collaboration framework that synergizes large and small language models to achieve high-quality reasoning with significantly reduced computational cost.
Outcome:	The proposed framework outperforms the mentor LLM while preserving the benefits of the thinking paradigm of LLMs.

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Huimin Wang, Yutian Zhao, Yefeng Zheng, Xian Wu

Responsible & Trustworthy NLP

Challenge:	RareSyn is a data synthesis approach to augment and de-identify EHRs with a focus on rare diseases.
Approach:	They propose a data synthesis approach to augment and de-identify EHRs with a focus on rare diseases.
Outcome:	The proposed model augments and de-identifies EHRs with a focus on rare diseases.

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Dialogue Systems & Conversational Agents

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)

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Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao

DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)

Challenge:	X-ray and CT are the gold standard for COVID-19 diagnosis and treatment . however, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists.
Approach:	They propose to use X-ray and CT to generate medical reports automatically . they evaluate DeltaNet on a COVID-19 dataset, where it outperforms state-of-the-art approaches .
Outcome:	The proposed system outperforms state-of-the-art methods on a COVID-19 dataset.

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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng

Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)

Challenge:	Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages.
Approach:	They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation .
Outcome:	The proposed model outperforms existing state-of-the-art methods on two benchmark datasets.

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Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Information Extraction & Text Mining

Challenge:	Distant supervision models suffer from high label noise and are not reliable for DS.
Approach:	They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF.
Outcome:	The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function.

Training-free LLM Merging for Multi-task Learning (2025.acl-long)

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Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao

Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing tasks.
Approach:	They propose a training-free method for unifying different specialized LLMs into a single model using model-wise and layer-wise pruning and scaling.
Outcome:	The proposed method outperforms existing merging techniques and surpasses models fine-tuned on combined datasets in most scenarios.

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Lei Gao, Xinnan Zhang, Xian Wu, Shen Ge, Yefeng Zheng

Structured Data in NLP

Challenge:	Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue.
Approach:	They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items .
Outcome:	The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set.

Refining BERT Embeddings for Document Hashing via Mutual Information Maximization (2021.findings-emnlp)

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Zijing Ou, Qinliang Su, Jianxing Yu, Ruihui Zhao, Yefeng Zheng, Bang Liu

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)

Challenge:	Existing unsupervised document hashing methods are mostly established on generative models . due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly .
Approach:	They propose to learn hash codes from BERT embeddings by modifying existing models . they use mutual information maximization principle to maximize mutual information .
Outcome:	The proposed method outperforms existing methods learned from BERT embeddings on three benchmark datasets.

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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian

Knowledge Representation

Challenge:	Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events.
Approach:	They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events.
Outcome:	The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making .

T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Question Answering

Challenge:	Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process.
Approach:	They propose a framework that dynamically adapts reasoning depth based on question complexity.
Outcome:	Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%.

CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation (2023.acl-long)

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Huimin Wang, Wai Chung Kwan, Kam-Fai Wong, Yefeng Zheng

Challenge:	Automated diagnosis (AD) is a critical application of AI in healthcare . despite its simplicity and superior performance, a decline in disease diagnosis accuracy is observed .
Approach:	They propose a new collaborative disease and symptom generation framework to improve automatic diagnosis.
Outcome:	The Transformer-based method achieves an average 2.3% improvement over previous state-of-the-art methods . it can be used to query patients about their symptoms and health concerns .

Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)

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Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng

Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)

Challenge:	Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways.
Approach:	They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods.
Outcome:	The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets.

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Zhenxi Lin, Ziheng Zhang, Meng Wang, Yinghui Shi, Xian Wu, Yefeng Zheng

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Challenge:	Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity.
Approach:	They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions.
Outcome:	The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions.

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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

Reasoning

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.

Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)

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Zhenxi Lin, Ziheng Zhang, Jian Wu, Yefeng Zheng, Xian Wu

ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification (2023.findings-eacl)

Challenge:	Existing attempts to apply large language models to BioEL have revealed difficulties .
Approach:	They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities .
Outcome:	Extensive experiments show that the framework outperforms existing LLMs.

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Wangjie Jiang, Zhihao Ye, Bang Liu, Ruihui Zhao, Jianguang Zheng, Mengyao Li, Zhiyong Li, Yujiu Yang, Yefeng Zheng

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

Challenge:	Existing methods for relation classification suffer from the scarcity of manually annotated data.
Approach:	They propose a novel relation classification model that incorporates query representation into the encoding of novel prototypes and utilizes iteratively to achieve more interaction.
Outcome:	The proposed model outperforms the state-of-the-art model on two benchmark datasets.

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

Responsible & Trustworthy NLP

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning (2024.lrec-main)

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Wai-Chung Kwan, Huimin Wang, Hongru Wang, Zezhong Wang, Bin Liang, Xian Wu, Yefeng Zheng, Kam-Fai Wong

Dialogue Systems & Conversational Agents

Challenge:	Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be.
Approach:	They propose a joint Transformer-based model that generates a token-grained policy that allows more dynamic dialogue action generation without the need for predefined action candidates.
Outcome:	The proposed model outperforms existing models showing improvements of 9% and 13% in success rate and 34% and 37% in diversity of dialogue actions across two benchmark dialogue modeling tasks.

Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)

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Zhihong Zhu, Yunyan Zhang, Xuxin Cheng, Zhiqi Huang, Derong Xu, Xian Wu, Yefeng Zheng

Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)

Challenge:	Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level.
Approach:	They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization.
Outcome:	The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments.

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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng

Biomedical Entity Linking as Multiple Choice Question Answering (2024.lrec-main)

Challenge:	Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal.
Approach:	They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs.
Outcome:	The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs.

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Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng

A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)

Challenge:	Existing methods for biomedical entity linking are discriminative and disambiguative . Existing models for bioMEDical entity linking use a BERT-based encoder to encode mentions and entities into the same embedding space and dissociate mentions by nearest neighbors.
Approach:	They propose a model that treats biomedical entity linking as Multiple Choice Question Answering.
Outcome:	The proposed model outperforms state-of-the-art models on several datasets.

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Fan Zhang, Hao Chen, Zhihong Zhu, Ziheng Zhang, Zhenxi Lin, Ziyue Qiao, Yefeng Zheng, Xian Wu

Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)

Challenge:	Existing single-cell foundation language models are based on pre-trained and large language models.
Approach:	They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms .
Outcome:	The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks.

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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Pre-trained models perform poorly with limited data and rare biomedical words.
Approach:	They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach.
Outcome:	The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)

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.

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Yutian Zhao, Huimin Wang, Xian Wu, Yefeng Zheng