Papers by Joe Yu
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes (2025.naacl-short)
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Isabel O. Gallegos, Ryan Aponte, Ryan A. Rossi, Joe Barrow, Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt, Nedim Lipka, Deonna Owens, Jiuxiang Gu
| Challenge: | Large language models exhibit harmful social biases, but they are often difficult to train and modify. |
| Approach: | They leverage the zero-shot capabilities of large language models to reduce stereotyping . they introduce a technique called zero- shot self-debiasing to reduce bias . |
| Outcome: | The proposed technique reduces stereotyping across nine different social groups while relying on the LLM itself and a simple prompt. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection (2026.acl-srw)
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| Challenge: | Existing models lack finegrained classification schemes to determine whether small changes impact legal obligations or merely update formatting. |
| Approach: | They propose a benchmark for change detection in EU regulations that uses 4,772 manually annotated pairs of structurally distinct provisions mapped to a six-class taxonomy of legal change types. |
| Outcome: | The proposed framework combines lexical algorithms, dense encoders, and Large Language Models (LLMs) as baselines. |
Generation of Patient After-Visit Summaries to Support Physicians (2022.coling-1)
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Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu
| Challenge: | After-visit summary is a summary note given to patients after their clinical visit. |
| Approach: | They propose to automate the generation of after-visit summaries and introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture important details of the clinical notes. |
| Outcome: | The proposed system improves on a large clinical dataset that contains electronic health record (EHR) notes and their associated summaries. |
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment (2025.naacl-long)
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Sangwon Yu, Jongyoon Song, Bongkyu Hwang, Hoyoung Kang, Sooah Cho, Junhwa Choi, Seongho Joe, Taehee Lee, Youngjune Gwon, Sungroh Yoon
| Challenge: | Experimental results show that large language models exhibit a negative bias in binary decision tasks . hallucination is a factor that degrades reliability of LLMs . |
| Approach: | They propose a negative attention score to systematically and quantitatively formulate negative bias by using a parameter-efficient fine-tuning technique. |
| Outcome: | The proposed method reduces the gap between precision and recall caused by negative bias while preserving generalization abilities. |
2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset Download PDF (2025.findings-acl)
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Marta R. Costa-jussà, Bokai Yu, Pierre Andrews, Belen Alastruey, Necati Cihan Camgoz, Joe Chuang, Jean Maillard, Christophe Ropers, Arina Turkatenko, Carleigh Wood
| Challenge: | We extend the BELEBELE dataset to speech and sign, and extend the Automatic Speech Recognition Benchmark, FLEURS, by 20%. |
| Approach: | They extend the BELEBELE and FLEURS speech comprehension datasets to speech and sign . they evaluate the datasets for 5-shot and zero-shot settings and find that the accuracy is 10% lower than reading comprehension. |
| Outcome: | The proposed dataset covers 91 spoken languages and one sign language (ASL) it also extends the Automatic Speech Recognition Benchmark, FLEURS, by 20% across languages. |