Papers by Ya Wang
HSDreport: Heart Sound Diagnosis with Echocardiography Reports (2024.findings-emnlp)
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Zihan Zhao, Pingjie Wang, Liudan Zhao, Yuchen Yang, Ya Zhang, Kun Sun, Xin Sun, Xin Zhou, Yu Wang, Yanfeng Wang
| Challenge: | Existing methods for heart sound diagnosis are limited to a few fixed categories and do not utilize echocardiography reports, the gold standard in the diagnosis of related diseases. |
| Approach: | They propose a benchmark that mandates the direct utilization of heart sounds obtained from auscultation to predict echocardiography reports. |
| Outcome: | The proposed method outperforms existing methods and existing multimodal LLMs in detecting key abnormalities in heart sounds. |
RaTEScore: A Metric for Radiology Report Generation (2024.emnlp-main)
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| Challenge: | Existing metrics to evaluate the quality of medical reports are limited due to the complexity of clinical free-form texts. |
| Approach: | They propose a new metric to assess the quality of medical reports generated by AI models. |
| Outcome: | The proposed metric is based on a medical NER dataset and trained on NER models . it aligns more closely with human preference than existing metrics, the authors show . |
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation (2025.acl-long)
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Xiechi Zhang, Zetian Ouyang, Linlin Wang, Gerard De Melo, Zhu Cao, Xiaoling Wang, Ya Zhang, Yanfeng Wang, Liang He
| Challenge: | Existing evaluation methods based on large language models (LLMs) are expensive and lack expertise due to limitations in human expertise. |
| Approach: | They propose an open-source automatic evaluation model with 13B parameters specifically engineered to measure the question-answering proficiency of medical LLMs. |
| Outcome: | The proposed model surpasses baselines in terms of correlation with human judgments. |
Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)
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| Challenge: | Code large language models (LLMs) enhance programming by understanding and generating code across languages. |
| Approach: | a new benchmark evaluates code understanding and generation in repositories using code large language models. |
| Outcome: | The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages. |
Dynamic Energy-Based Contrastive Learning with Multi-Stage Knowledge Verification for Event Causality Identification (2025.emnlp-main)
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| Challenge: | Existing methods for event causal identification rely on rule-based or random sampling strategies, which introduce spurious causal positives. |
| Approach: | They propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge verification which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmarks. |
Miner: Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models (2026.acl-long)
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| Challenge: | Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts. |
| Approach: | They propose a method that repurposes the policy’s intrinsic uncertainty as a self-supervised reward signal, with no external supervision, auxiliary models, or additional inference cost. |
| Outcome: | Evaluated across six reasoning benchmarks on Qwen3-4B and Qwend3-8B base models, the proposed method achieves state-of-the-art performance among the other four methods. |
DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics (2024.findings-acl)
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| Challenge: | Structured data processing is a complex and complex process. |
| Approach: | They propose a framework that captures heterogeneity of structured data using large language models . they propose group positional encoding, hierarchical attention bias and optimal transport alignment layer . |
| Outcome: | The proposed framework outperforms baseline methods and few-shot GPT-4 on a medical lab report dataset. |
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to reliability of large language models often lack self-correction or use costly post-hoc verification. |
| Approach: | They propose a decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. |
| Outcome: | Extensive experiments across five benchmarks show the proposed framework improves truthfulness and factual accuracy. |
Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)
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| Challenge: | Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures. |
| Approach: | They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling . |
| Outcome: | The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets. |
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach (2026.findings-acl)
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| Challenge: | Existing methods to enhance medical reasoning lack high-quality data. |
| Approach: | They propose a medical knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework that uses rare disease knowledge to synthesize distribution-controllable reasoning questions. |
| Outcome: | The proposed method outperforms existing methods across ten medical benchmarks and achieves up to 5.93% gain on rare diseases tasks. |
The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas (2025.emnlp-main)
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| Challenge: | Existing evaluations of LLMs' moral reasoning capabilities rely on single-step evaluations, ignoring how models adapt to evolving ethical challenges. |
| Approach: | They propose a framework to evaluate evolving moral judgments of large language models (LLMs) using multi-step moral dilemma questionnaires. |
| Outcome: | The proposed framework enables a fine-grained analysis of how LLMs adjust their moral reasoning across escalating dilemmas. |
Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)
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| Challenge: | Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems . |
| Approach: | They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction . |
| Outcome: | The proposed model outperforms existing models on REG and REC tasks. |
An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks (2022.emnlp-main)
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| Challenge: | Position Embedding (PE) is essential for transformer to capture the sequence ordering of input tokens. |
| Approach: | They propose a unified position embedding method that bridges the semantic gap between modalities and embeds the anchor-based distance to guide computation of cross-attention. |
| Outcome: | The proposed method obtains new SOTA results on a wide range of benchmarks. |
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios (2024.emnlp-main)
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| Challenge: | Chinese medical large language models (LLMs) are underperforming on this benchmark, especially where medical reasoning and factual consistency are vital. |
| Approach: | They propose a benchmark with 14 expert-guided clinical scenarios to assess the medical ability of large language models across 7 pivot dimensions. |
| Outcome: | The proposed benchmark has been validated in several ways. |