Papers by Rongxin Zhu
Factual Dialogue Summarization via Learning from Large Language Models (2025.coling-main)
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| Challenge: | Existing models generate fluent and coherent summaries, but inconsistencies can be found in generated summary. |
| Approach: | They propose to use symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization. |
| Outcome: | The proposed model outperforms baseline models in BART, PEGASUS, and Flan-T5 in factual consistency and accuracy. |
FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation (2026.findings-eacl)
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Yulia Otmakhova, Thinh Hung Truong, Rahmad Mahendra, Zenan Zhai, Rongxin Zhu, Daniel Beck, Jey Han Lau
| Challenge: | FLUKE introduces controlled variations across linguistic levels and leverages large language models with human validation to generate modifications. |
| Approach: | They propose a framework for assessing model robustness through systematic minimal variations of test data. |
| Outcome: | The proposed framework evaluates models and LLMs across six diverse NLP tasks and shows that they are more robust to natural, fluent modifications than base models. |
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)
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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li
| Challenge: | Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes. |
| Approach: | They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy. |
| Outcome: | The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline. |
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
| Outcome: | The proposed model performs on par with the state-of-the-art models while requiring fewer resources. |