Papers by Jimeng Sun
Explainable Prediction of Medical Codes from Clinical Text (N18-1)
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| Challenge: | Clinical notes are text documents that are created by clinicians for each patient encounter. |
| Approach: | They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. |
| Outcome: | The proposed method is accurate and better than the current state of the art. |
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning (2024.emnlp-main)
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| Challenge: | Current efforts in interpretability of medical coding rely heavily on label attention mechanisms, which often leads to the highlighting of extraneous tokens irrelevant to the ICD code. |
| Approach: | They propose to leverage dictionary learning to extract sparsely activated representations from dense language models embedded in superposition to facilitate accurate interpretability. |
| Outcome: | The proposed model extracts sparsely activated representations from dense language models in superposition, even when the highlighted tokens are medically irrelevant. |
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models (2024.acl-long)
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| Challenge: | Large language models suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. |
| Approach: | They propose a pipeline that leverages knowledge graphs to enhance LLMs’ inference and transparency by eliciting the mind map of LLM's, which reveals their reasoning pathways based on the ontology of knowledge. |
| Outcome: | The proposed pipeline enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. |
s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)
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| Challenge: | Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models. |
| Approach: | They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward. |
| Outcome: | The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples. |
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model. |
| Approach: | They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model. |
| Outcome: | The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets. |
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have advanced tasks like text summarization, but their size and computational demands limit their use in resource-constrained and privacy-centric settings. |
| Approach: | They propose a framework for distilling LLMs’ text summarization abilities into a compact, local model using a curriculum learning strategy that evolves from simple to complex tasks. |
| Outcome: | The proposed framework outperforms baseline models on CNN/DailyMail, XSum, and ClinicalTrial, and improves interpretability by providing insights into the summarization rationale. |
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)
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| Challenge: | Existing relation extraction methods rely on exact matching with human-annotated reference relations, while GRE methods produce diverse and semantically accurate relations. |
| Approach: | They propose a multi-dimensional assessment of relation extraction methods using human-annotated reference relations. |
| Outcome: | The proposed method is consistent with human preferences for RE quality. |
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise (2025.emnlp-main)
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Hanyin Wang, Chufan Gao, Qiping Xu, Bolun Liu, Guleid Hussein, Hariprasad Reddy Korsapati, Mohamad El Labban, Kingsley Iheasirim, Mohamed Hassan, Gokhan Anil, Brian Bartlett, Jimeng Sun
| Challenge: | Currently, no automated, scalable method exists to evaluate the quality of LLM-generated clinical notes, leaving manual evaluation the gold standard. |
| Approach: | They propose a framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes. |
| Outcome: | The proposed framework outperforms reasoning and non-reasoning models on key evaluations and selects physician-preferred clinical notes with 56.2% accuracy. |
AutoTrial: Prompting Language Models for Clinical Trial Design (2023.emnlp-main)
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| Challenge: | Generative large language models (LLMs) are a popular tool for creating coherent and human-like documents for clinical trials. |
| Approach: | They propose to generate clinical eligibility criteria using language models by a hybrid of discrete and neural prompting and scalable knowledge incorporation via in-context learning. |
| Outcome: | The proposed method generates high-quality criteria texts fluent and coherent with high accuracy against the GPT-3.5 baselines. |
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (2022.emnlp-main)
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| Challenge: | Existing vision-text contrastive learning methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. |
| Approach: | They propose to decouple medical image-text contrastive learning and replace it with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive training. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval with only 20K pre-training data. |
PILOT: Legal Case Outcome Prediction with Case Law (2024.naacl-long)
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| Challenge: | predicting legal case outcomes requires identifying relevant precedent cases . predicting case outcomes in case law systems presents unique challenges . |
| Approach: | They propose a framework for making legal case outcome predictions with case law . they propose to use two modules for relevant case retrieval and temporal pattern handling . |
| Outcome: | The proposed framework shows significant improvement over previous models based on civil law cases . it is crucial to identify relevant precedent cases that serve as evidence for judges . |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)
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| Challenge: | Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. |
| Approach: | They propose a novel approach that integrates a trainable memory module with a noisy-robust loss function that accounts for the positive-unlabeled setting to unlock the full potential of large-scale noisy training data. |
| Outcome: | The proposed model outperforms existing methods on a ReDocRED benchmark dataset with an absolute F1 score improvement of over 3%. |
SECRET: Semi-supervised Clinical Trial Document Similarity Search (2025.acl-long)
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| Challenge: | Clinical trials are resource-intensive, time-consuming and expensive to conduct, where errors in trial design, reduced efficacy, and safety events can result in significant delays, financial losses, and damage to reputation. |
| Approach: | They propose a method to summarize clinical trial protocols and search for similar trials based on a query protocol to identify similar historical trials. |
| Outcome: | The proposed method significantly outperforms baselines in recall@1 and precision@1 searches and zero-shot patient-trial matching, highlighting its superior utility in these tasks. |
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (2022.emnlp-main)
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| Challenge: | Existing methods for generating longitudinal multimodal EHRs are limited due to privacy concerns. |
| Approach: | They propose to generate longitudinal multimodal EHRs by unconditional generation or longitudinal inference . existing methods generate single-modal E HRs by conditional generation or by longitudinal inferment . |
| Outcome: | The proposed method is more flexible and controllable than existing methods and is more cost-effective than existing ones. |
Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation (2025.findings-acl)
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Hanyin Wang, Chufan Gao, Bolun Liu, Qiping Xu, Guleid Hussein, Mohamad El Labban, Kingsley Iheasirim, Hariprasad Reddy Korsapati, Chuck Outcalt, Jimeng Sun
| Challenge: | Proprietary Large Language Models (LLMs) have demonstrated promising capabilities in clinical text summarization tasks. |
| Approach: | They propose a domain- and task-specific adaptation process for an open-source LLaMA-2 model . LLama-2 can generate high-quality clinical notes from outpatient patient-doctor dialogues . |
| Outcome: | The proposed model can generate clinical notes comparable to those authored by physicians. |
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision (2022.findings-emnlp)
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| Challenge: | Clinical trials are expensive and time-consuming to conduct, and lengthy trial documents and lack of labeled data make comparisons difficult. |
| Approach: | They propose a zero-shot clinical trial retrieval method which learns through self-supervision without the need for annotating similar clinical trials. |
| Outcome: | The proposed method improves on baselines on precision/recall and 15% on the downstream trial outcome prediction task. |
DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (2023.emnlp-demo)
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Daniel Hajialigol, Derek Kaknes, Tanner Barbour, Daphne Yao, Chris North, Jimeng Sun, David Liem, Xuan Wang
| Challenge: | Medical claim coding is the process of transforming medical records into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. |
| Approach: | They propose an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs) a novel multi-task Transformer model allows users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. |
| Outcome: | The proposed system allows users to analyze the weights of the input and compare across multiple discharge summaries. |
Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation (2024.emnlp-main)
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| Challenge: | Currently, the most commonly used confidence score is the likelihood of the generated sequence . different tokens should be weighted differently depending on the context. |
| Approach: | They propose to assign different weights to various tokens using attention values elicited from the base LLM. |
| Outcome: | The proposed model improves the confidence of the predicted sequence probability by assigning weights to tokens based on attention values elicited from the base model. |
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. |