Papers by Shen Ge
Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias. |
| Approach: | They propose a model that compares the current input image with normal images to capture abnormal regions by contrasting the input image and normal images. |
| Outcome: | The proposed model can be easily incorporated into existing models to boost their performance under most metrics. |
Competence-based Multimodal Curriculum Learning for Medical Report Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Medical report generation is more challenging for data-driven neural models due to data bias and limited medical data. |
| Approach: | They propose a Competence-based Multimodal Curriculum Learning framework to alleviate the data bias by efficiently utilizing the limited medical data for medical report generation. |
| Outcome: | The proposed framework can be incorporated into existing models to improve their performance on the IU-Xray and MIMIC-CXR datasets. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
| Outcome: | The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. |
Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses (2026.acl-long)
Copied to clipboard
Chongyuan Dai, Yaling Shen, Zihan Gao, Jia Li, Yishun Jiang, Yaxiong Wang, Liu Liu, Zongyuan Ge, Jinpeng Hu
| Challenge: | Culture is a fundamental determinant of human affective processing and affective perceptions are often limited by declarative knowledge or established societal customs. |
| Approach: | They propose a multimodal benchmark that leverages LLM-generated provisional labels to isolate cross-cultural emotional distinctions. |
| Outcome: | The proposed benchmark captures cross-cultural emotional distinctions and derives reliable ground-truth annotations through human evaluation. |
From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora (2025.emnlp-main)
Copied to clipboard
| Challenge: | Experiments show that models trained on multi-way parallel data outperform those trained on unaligned data. |
| Approach: | They propose a large-scale, high-quality multi-way parallel corpus based on TED Talks that spans 113 languages with up to 50 languages aligned in parallel. |
| Outcome: | The proposed model outperforms models trained on unaligned multilingual data on six multilingual benchmarks. |
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis (2022.coling-1)
Copied to clipboard
Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge, Yangtian Yan, Xingwang Wu, Yefeng Zheng, S. Kevin Zhou, Li Xiao
| 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. |
PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics (2026.findings-acl)
Copied to clipboard
Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge
| Challenge: | Mental disorders affect nearly one in seven people worldwide, yet the vast majority do not receive adequate care. |
| Approach: | They propose a framework to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations. |
| Outcome: | Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness. |
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)
Copied to clipboard
Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang
| Challenge: | Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning. |
| Approach: | They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations. |
| Outcome: | The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
Copied to clipboard
Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
Copied to clipboard
| 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. |
Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment. |
| Approach: | They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment. |
| Outcome: | The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency. |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
Copied to clipboard
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |
WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing MCoT methods focus on inter-object reasoning, overlooking intra-object understanding crucial for image classification. |
| Approach: | They propose a Weak-supervision-guided Step-by-step Explanation method that reformulates MCoTs under weak supervision into concise, interpretable reasoning chains. |
| Outcome: | The proposed method improves interpretability by 37% and improves classification accuracy. |
InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)
Copied to clipboard
| Challenge: | InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Approach: | They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Outcome: | The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws. |
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents . |
| Approach: | They propose a new Spoken Conversational Question Answering task to model human conversations . they propose DDNet, which ingests cross-modal information to achieve fine-grained representations of speech and language modalities. |
| Outcome: | The proposed method achieves superior performance in spoken conversational question answering. |
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for video captioning consider a sequence of frames and biases towards focused objects. |
| Approach: | They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption . |
| Outcome: | The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed. |
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)
Copied to clipboard
| Challenge: | OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement. |
| Approach: | They propose a family of open-source code systems for generating, executing, and iteratively refining code. |
| Outcome: | The OpenCodeInterpreter-33B performs well on humanEval, MBPP, and EvalPlus benchmarks. |