Papers by Fenglong Ma
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (2021.acl-long)
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| Challenge: | Existing methods for medical image analysis use predefined template databases or ignore hierarchical nature of medical report generation. |
| Approach: | They propose a hierarchical retrieval mechanism to extract both report and sentence-level templates for clinically accurate report generation. |
| Outcome: | The proposed model extracts both report and sentence-level templates for clinically accurate report generation. |
Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources (2024.acl-long)
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Xiaochen Wang, Junyu Luo, Jiaqi Wang, Yuan Zhong, Xiaokun Zhang, Yaqing Wang, Parminder Bhatia, Cao Xiao, Fenglong Ma
| Challenge: | Current pre-training techniques rely on a limited scope of medical data, limiting the range of downstream tasks. |
| Approach: | They propose a pre-training strategy that unifies patient data within individual sources and captures explicit and implicit correlations between patients across different sources. |
| Outcome: | The proposed strategy bridges the gap between multimodal medical sources by aggregating patient data within individual sources and capturing explicit and implicit correlations between patients across sources. |
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
| Outcome: | The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models. |
AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate coherent reasoning paths before conclusions, but they introduce new vulnerabilities. |
| Approach: | They propose a framework that leverages a weaker but less-aligned model to simulate execution reasoning for initial hijacking attempts and iteratively refines attacks by exploiting reasoning patterns leaked through the target LRM’s refusals. |
| Outcome: | The proposed framework achieves 100% success rate within one or few turns, neutralizing reasoning-based defenses even when evaluated by robustly aligned external models. |
GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models (2026.acl-industry)
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| Challenge: | a growing number of online grocery shoppers are using category-level recommendation systems . traditional item-level methods face scalability and accuracy challenges . |
| Approach: | a new language model is developed to encode cyclical purchasing patterns into model parameters . the model is scalable and more business-aligned than traditional item-level methods . |
| Outcome: | a new language model outperforms standard methods in a live production environment . the proposed model achieves a 7.5% relative improvement in cart-adds per impression . |
AdaDHP: Fine-Grained Fine-Tuning via Dual Hadamard Product and Adaptive Parameter Selection (2025.acl-long)
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| Challenge: | Increasing number of parameters can be challenging under resource-constrained environments. |
| Approach: | They propose a parameter-efficient fine-tuning method with fewer parameters and finer granularity that can adaptively select important parameters for each task. |
| Outcome: | The proposed method can fine-tune important parameters for each task, while maintaining the same weights. |
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models (2024.emnlp-main)
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| Challenge: | Existing foundation models are limited in access to diverse modalities and privacy regulations restrict the development of comprehensive foundation models. |
| Approach: | They propose a knowledge injection approach to extract and inject healthcare knowledge into medical foundation models to enhance their ability to handle multiple tasks and modalities. |
| Outcome: | The proposed method preserves privacy and enhances the model’s ability to handle complex medical tasks involving multiple modalities. |
Can Factual Opinions Be Edited (Manipulated) in Large Language Models? (2026.acl-long)
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| Challenge: | Existing methods for factual opinion editing focus on atomic facts, ignoring the risks associated with factual opinions. |
| Approach: | They propose a method that achieves opinion–evidence alignment without relying on explicit instructions to edit factual opinions. |
| Outcome: | The proposed method achieves opinion–evidence alignment without relying on explicit instructions. |
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)
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| Challenge: | Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios. |
| Approach: | They construct a Chinese medical dialogue dataset based on real medical consultations. |
| Outcome: | The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue. |
Knowledge-Guided Paraphrase Identification (2021.findings-emnlp)
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| Challenge: | Existing methods for paraphrase identification (PI) are limited due to lack of professional knowledge. |
| Approach: | They propose to leverage Wikipedia knowledge to accurately identify paraphrases by mining outline knowledge of given sentences from Wikipedia. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two public datasets: PARADE and clinicalSTS2019. |
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning (2025.acl-long)
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| Challenge: | Existing methods rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. |
| Approach: | They propose a framework for automatic attention alignment tuning that leverages weak labels from SAM and selectively modifies visually-critical attention heads to improve alignment while minimizing interference. |
| Outcome: | The proposed framework outperforms state-of-the-art models on medical VQA and report generation benchmarks. |
Shadow-Activated Backdoor Attacks on Multimodal Large Language Models (2025.findings-acl)
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Ziyi Yin, Muchao Ye, Yuanpu Cao, Jiaqi Wang, Aofei Chang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma
| Challenge: | Existing backdoor attacks on Multimodal Large Language Models are less applicable to open-ended conversations with users. |
| Approach: | They propose a shadow-activated backdoor attack scenario where attackers inject malicious content into the responses of MLLMs when the responses explicitly relate to the shadowed object. |
| Outcome: | The proposed framework achieves the desired behaviors by constructing a poisoned dataset and implementing an attention-regularized tuning strategy. |
Zero-Resource Hallucination Prevention for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for detecting hallucinations post-generation suffer from inconsistent performance due to the influence of instruction format and model style. |
| Approach: | They propose a new technique that evaluates the model’s familiarity with the concepts present in the input instruction and withholding the generation of response in case of unfamiliar concepts under the zero-resource setting. |
| Outcome: | The proposed technique shows superior performance across four different large language models and demonstrates that it can be used to mitigate hallucinations in LLMs. |
ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
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| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |
Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought (2026.findings-acl)
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| Challenge: | Existing heuristics fail to capture global causal logic due to rigid rules and limited search spaces. |
| Approach: | They propose a framework that extracts the essential logical structure from reasoning chains. |
| Outcome: | Experiments show that Pru-CoT models generate more compact reasoning paths compared to models trained on verbose data. |
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)
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| Challenge: | Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language. |
| Approach: | They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure. |
| Outcome: | The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines. |
Hierarchical Pretraining on Multimodal Electronic Health Records (2023.emnlp-main)
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| Challenge: | Existing pretraining models on EHR data are too specific, limiting their transferability. |
| Approach: | They propose a general, unified pretraining framework for hierarchically multimodal EHR data that can be used to train models on a large dataset before fine-tuning it on 'upstream' tasks. |
| Outcome: | The proposed model performs on eight downstream tasks spanning three levels and compares with baselines on 18 different tasks. |
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited by computational and storage requirements. |
| Approach: | They propose a budget-guided iterative search strategy to disentangle binary module and rank dimension search spaces and early selection strategies based on parameter budgets. |
| Outcome: | The proposed method significantly improves search efficiency on public benchmarks. |
Multi-grained Named Entity Recognition (P19-1)
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| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
| Approach: | They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. |
| Outcome: | The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks. |
Fusion: Towards Automated ICD Coding via Feature Compression (2021.findings-acl)
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| Challenge: | Existing methods to assign ICD codes from unstructured clinical notes are noisy and prone to errors. |
| Approach: | They propose a feature compressed ICD coding model called Fusion to address this problem. |
| Outcome: | The proposed model outperforms existing models on two widely used datasets. |