Papers by Yuchen Yuan
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
| Challenge: | Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences. |
| Approach: | They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks . |
| Outcome: | The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency. |
On Attention Redundancy: A Comprehensive Study (2021.naacl-main)
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| Challenge: | Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. |
| Approach: | They propose a multi-layer multi-head self-attention mechanism which is widely applied in modern neural language models. |
| Outcome: | The proposed model is useful for interpretation and model compression. |
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)
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Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan
| Challenge: | Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability. |
| Approach: | They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic. |
| Outcome: | The proposed model outperforms open-source models and achieves competitive performance to closed-source model. |
Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding. |
| Approach: | They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks. |
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)
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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
| Challenge: | Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations. |
| Approach: | They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation. |
| Outcome: | The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement. |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
Unsupervised Grammatical Error Correction Rivaling Supervised Methods (2023.emnlp-main)
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| Challenge: | Current state-of-the-art grammatical error correction systems rely on labeled data . current systems require manual correction and require a large quantity of labeles . |
| Approach: | They propose an unsupervised method to build a grammatical error correction system using a fixer and a critic. |
| Outcome: | The proposed system outperforms previous unsupervised systems on English and Chinese GEC. |
PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)
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| Challenge: | We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery . |
| Approach: | They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries . |
| Outcome: | The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery . |
FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models (2026.acl-long)
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| Challenge: | FinChart-Bench is the first benchmark specifically focused on real-world financial charts. |
| Approach: | They propose a benchmark specifically focused on real-world financial charts. |
| Outcome: | The proposed benchmark evaluates 26 state-of-the-art LVLMs on FinChart-Bench. |