Papers by Junying Chen
LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture (2025.findings-emnlp)
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Xidong Wang, Dingjie Song, Shunian Chen, Junying Chen, Zhenyang Cai, Chen Zhang, Lichao Sun, Benyou Wang
| Challenge: | Long-context Large Language Models (MLLMs) are critical for video understanding and image analysis. |
| Approach: | They propose a hybrid architecture that integrates Mamba and Transformer blocks . they introduce data construction methods that capture both temporal and spatial dependencies . |
| Outcome: | The proposed model achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption. |
DentalGPT: Incentivizing Multimodal Reasoning in Dentistry (2026.findings-acl)
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Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng, Yanchao Li, Liang Jingyi, Junying Chen, Yunjin Yang, Jiajun You, Shuzhi Deng, null Xieruiqiii, Yuanting Chen, Xiangyi Feng, Jianquan Li, Liangyi Chen, Junwen Wang, Shan Jiang, Benyou Wang
| Challenge: | Current multimodal large language models (MLLMs) show limited understanding of dental images. |
| Approach: | They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning. |
| Outcome: | The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks. |
Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems (2025.findings-acl)
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| Challenge: | OpenAI o1 has been a significant milestone in large language model development . however, most research in reasoning has focused on mathematical tasks . medical domains require robust reasoning to provide reliable answers . |
| Approach: | They propose a method to verify medical reasoning using a medical verifier . they also propose RL and reinforcement learning to enhance reasoning . |
| Outcome: | The proposed method outperforms general and medical-specific baselines using only 40K verifiable problems. |
Unlocking LLMs’ Self-Improvement Capacity with Autonomous Learning for Domain Adaptation (2025.findings-acl)
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| Challenge: | Existing models that use self-supervised and instruction fine-tuning can be trained using unlabeled corpora. |
| Approach: | They propose to use unlabeled target corpora to adapt large language models to new domains . they propose to employ self-supervised pre-training and instruction fine-tuning methods . |
| Outcome: | The proposed model can adapt to new domains using only a large amount of unlabeled target corpora. |
Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale (2024.emnlp-main)
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Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Chen, Xidong Wang, Zhenyang Cai, Ke Ji, Xiang Wan, Benyou Wang
| Challenge: | Multimodal large language models (MLLMs) lack visual knowledge in medical applications due to data privacy concerns and high annotation costs. |
| Approach: | They refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) to denoise and reformat the data. |
| Outcome: | The proposed model significantly improves the MMMU Health & Medicine track and shows that it can be used in multimodal scenarios. |
HuatuoGPT, Towards Taming Language Model to Be a Doctor (2023.findings-emnlp)
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Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
| Challenge: | Experimental results show that the distilled language model outperforms its teacher model (ChatGPT) in most cases. |
| Approach: | They propose a Large Language Model (LLM) that leverages both distilled data from **ChatGPT** and real-world data from**doctors** in the supervised fine-tuning stage. |
| Outcome: | The proposed model outperforms the teacher model in most cases by using additional real-world data and RLMF to align the language model with the merits of both sources. |
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge. |
| Approach: | They propose a method for synthesizing diverse and high-quality RAG instruction data based on any source corpus. |
| Outcome: | The proposed method outperforms existing methods in multiple tasks and achieves strong zero-shot performance. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
Benchmarking LLMs on Authentic Cases from Medical Journals (2026.findings-acl)
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| Challenge: | Existing medical benchmarks suffer from performance saturation due to medical exam questions. |
| Approach: | They evaluate the performance of over 20 open-source and proprietary large language models and benchmark them against human medical experts. |
| Outcome: | The new benchmark is based on authentic clinical cases sourced from medical journals and implements rigorous human review process to ensure the quality and reliability of the benchmark. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)
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Wentao Ge, Shunian Chen, Hardy Chen, Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xie, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, Dingjie Song, Xidong Wang, Anningzhe Gao, Zhang Zhiyi, Jianquan Li, Xiang Wan, Benyou Wang
| Challenge: | Existing evaluation methodologies for multimodal large language models are limited in evaluating objective queries without considering real-world user experiences. |
| Approach: | They propose to evaluate multimodal large language models with per-sample criteria using potent MLLM as the judge. |
| Outcome: | The proposed evaluation paradigm shows that it can be used to evaluate multimodal large language models with per-sample criteria. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
CMB: A Comprehensive Medical Benchmark in Chinese (2024.naacl-long)
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Xidong Wang, Guiming Chen, Song Dingjie, Zhang Zhiyi, Zhihong Chen, Qingying Xiao, Junying Chen, Feng Jiang, Jianquan Li, Xiang Wan, Benyou Wang, Haizhou Li
| Challenge: | Large Language Models (LLMs) provide a great breakthrough in medicine, says a new study . existing studies on LLMs leverage subjective evaluation, but evaluation in medicine is professional . |
| Approach: | They propose a localized medical benchmark in Chinese rooted in native Chinese . they propose to use traditional Chinese medicine to evaluate large-scale LLMs . |
| Outcome: | a new benchmark is developed to evaluate large-scale LLMs in china . the proposed model is rooted in the native Chinese linguistic and cultural framework . |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis (2025.findings-acl)
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| Challenge: | Existing large language models (LLMs) are proving to be effective in medical automatic diagnosis, but their interpretability remains unaddressed. |
| Approach: | They propose to use a "Chain-of-Diagnosis" approach to enhance the interpretability of medical automatic diagnosis by outputting the disease confidence distribution. |
| Outcome: | The proposed model outperforms other LLMs on automatic diagnostic tasks across three real-world benchmarks and provides interpretability while ensuring controllability in diagnostic rigor. |
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically (2021.emnlp-main)
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| Challenge: | Existing methods to integrate knowledge into text can confuse the representation and import unexpected noises. |
| Approach: | They propose to leverage capsule routing to associate knowledge with medical literature hierarchically . they extract two fragments from medical literature and encode them into fragment representations . |
| Outcome: | The proposed method can more accurately associate knowledge with medical literature than mainstream methods. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |