Papers by John Wu
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. |
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
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Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)
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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi
| Challenge: | Currently, most medical data is generated and stored in unstructured, text-based format. |
| Approach: | They propose to use a patient phenotyping dataset to identify whether a given medical condition is present in their notes. |
| Outcome: | The proposed dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Similarity Analysis of Contextual Word Representation Models (2020.acl-main)
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| Challenge: | Existing and novel similarity measures are used to analyze contextual word representations . different architectures have rather similar representations, but different individual neurons. |
| Approach: | They propose a method to analyze contextual word representation models using similarity analysis. |
| Outcome: | The proposed approach can be used to analyze model similarity without external annotations. |