Papers by Yuyang Jiang
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts (2025.findings-acl)
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| Challenge: | Existing taxonomies are mainly constructed by experts or through crowd-sourcing, making the process time-consuming, labor-intensive, and restricted in coverage. |
| Approach: | They propose a method that leverages large language models to capture taxonomic structure . existing taxonomies are mainly constructed by experts or through crowd-sourcing . |
| Outcome: | Experiments on five real-world domains show that CodeTaxo outperforms state-of-the-art methods. |
QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing models that use multimodal inputs are often noisy or incomplete. |
| Approach: | They propose a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via aleatoric uncertainty. |
| Outcome: | The proposed framework is competitive or state-of-the-art across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-all property in practice. |
CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation (2025.findings-emnlp)
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Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li, Zecong Tang, Benjamin M. Mervak, Lydia Chelala, Christopher M Straus, Reve Chahine, Samuel G. Armato Iii, Chenhao Tan
| Challenge: | Existing metrics lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports. |
| Approach: | They propose a tabular framework with E**xpert-curated labels and an attribute-level comparison for radiology report evaluation (**CLEAR) |
| Outcome: | The proposed framework can extract clinical attributes and provide automated metrics that are strongly aligned with clinical judgment. |
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling (2021.emnlp-main)
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Baojun Wang, Zhao Zhang, Kun Xu, Guang-Yuan Hao, Yuyang Zhang, Lifeng Shang, Linlin Li, Xiao Chen, Xin Jiang, Qun Liu
| Challenge: | Existing approaches to integrate lexical knowledge into deep learning models are limited by large-scale dynamic lexicons. |
| Approach: | They propose a plug-in lexicon incorporation approach for BERT based sequence labeling tasks . they adopt word-agnostic tag embeddings to avoid re-training the representation . |
| Outcome: | The proposed framework achieves new SOTA even with large scale lexicons, the authors show . they adopt word-agnostic tag embeddings to avoid re-training the representation . |
GPT-4V Cannot Generate Radiology Reports Yet (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are becoming multimodal, and GPT-4 models are supposed to possess advanced skills across a wide range of domains, including high-stakes scenarios such as medicine. |
| Approach: | They perform a systematic evaluation of GPT-4 in generating radiology reports across three chest X-ray report benchmarks: MIMIC-CXR, CheXpert Plus, and IU X ray. |
| Outcome: | The proposed model fails in lexical and clinical efficacy metrics . the distributions of model-predicted labels remain constant regardless of groundtruth conditions on the image, suggesting that the model is not interpreting chest X-rays meaningfully. |
ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents (2026.acl-long)
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Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang
| Challenge: | Rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. |
| Approach: | They propose a peer review benchmarking tool based on paper-specific rubrics and a rubric-guided framework that decomposes reviewing into drafting and grounding stages. |
| Outcome: | The proposed framework outperforms baselines with stronger/larger backbones in both alignment with human judgments and rubric-based review quality across 8 dimensions. |