Papers by Fenglin Liu
Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)
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| 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. |
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)
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Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan
| Challenge: | Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios. |
| Approach: | They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o. |
| Outcome: | The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton
| Challenge: | commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools . |
| Approach: | They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression . |
| Outcome: | The proposed approach outperforms human experts in medical examinations on diverse datasets. |
Competence-based Multimodal Curriculum Learning for Medical Report Generation (2021.acl-long)
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| 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. |
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions (D18-1)
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| Challenge: | Existing approaches to image captioning combine visual and semantic attention to generate a detailed and comprehensive caption. |
| Approach: | They propose a stepwise image-topic merging network that integrates visual and semantic attentions to generate a detailed caption. |
| Outcome: | The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performance. |
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)
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| Challenge: | Existing approaches to generate informative titles for products with limited labels are inadequate for novel products. |
| Approach: | They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products. |
| Outcome: | The proposed approach achieves state-of-the-art results on novel product categories with limited labels. |
Federated Learning for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing methods to improve robustness of models focus on a single dataset . but, there are few studies on how to combine merits of different datasets . |
| Approach: | They propose a federated learning framework that could unify datasets and tasks . they propose MV-Encoder as backbone of the framework to provide multi-granularity text representations . |
| Outcome: | The proposed framework improves on two SLU benchmark datasets and federated learning settings. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)
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| Challenge: | Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language. |
| Approach: | They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles . |
| Outcome: | The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs. |
Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning (2025.emnlp-main)
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| Challenge: | a shortage of medical doctors limits access to timely and reliable healthcare . authors propose a multi-turn LLM-based medical assistant for medical inquiries . |
| Approach: | They propose a multi-turn LLM-based medical assistant that asks patients with patience . they compare it with SOTA one-shot and multi-turned LLMs to evaluate its performance . |
| Outcome: | The proposed medical assistant improves diagnostic accuracy, reduces uncertainty and enhances user experience. |
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
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| 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)
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| 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. |
Rethinking Skip Connection with Layer Normalization (2020.coling-main)
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| Challenge: | Existing methods to solve the optimization problem of deep neural networks are not linear, but can be used as a modulating mechanism between the input and output. |
| Approach: | They propose to use skip connection to adjust the scale of the input and output to improve the performance. |
| Outcome: | The proposed approach improves performance and convergence of deep neural networks and can be applied to machine translation and image classification datasets. |