Papers by Zhouhang Xie
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (2024.findings-acl)
Copied to clipboard
Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley
| Challenge: | Motivational Interviewing (MI) requires a system that can infer how to motivate users to adopt positive lifestyle changes. |
| Approach: | They propose a framework that can learn and apply conversation strategies from expert demonstrations by using natural language inductive rules. |
| Outcome: | The proposed framework outperforms in-context demonstrations that are over 50 times longer and can learn natural language strategies from demonstrations. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
Copied to clipboard
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. |
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision (2025.naacl-long)
Copied to clipboard
Zhouhang Xie, Tushar Khot, Bhavana Dalvi Mishra, Harshit Surana, Julian McAuley, Peter Clark, Bodhisattwa Prasad Majumder
| Challenge: | Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the discovery (i.e., goal). |
| Approach: | They propose a goal-oriented latent factor discovery system that integrates LLM’s instruction-following ability with statistical models to handle large, noisy datasets where LLM reasoning alone falls short. |
| Outcome: | The proposed system improves task performance by 5-52% over baselines and 1.8 times as often as the best alternative, on average, in human evaluation. |
GUI Agents: A Survey (2025.findings-acl)
Copied to clipboard
Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
Evaluating Language Model Pluralism through In-the-wild Crowd Discussions (2026.acl-long)
Copied to clipboard
Gagan Mundada, Rohan Surana, Nandhini Swaminathan, Bodhisattwa Prasad Majumder, Junda Wu, Julian McAuley, Zhouhang Xie
| Challenge: | Existing evaluation methods focus predominantly on multiple-choice and question-answering tasks, leaving open-ended generation largely unaddressed. |
| Approach: | They propose an evaluation framework that assesses LLM pluralism in open-ended generation by comparing outputs against free-form crowd responses. |
| Outcome: | The proposed evaluation framework decomposes ground-truth responses into atomic, non-overlapping claims and evaluates whether LLMs adequately cover this diverse claim space. |
Mitigating Hallucination in Fictional Character Role-Play (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Influence of parametric knowledge of large language models (LLMs) often causes role-playing characters to act out of character and hallucinate about things outside the scope of their knowledge. |
| Approach: | They propose a method that modulates the influence of parametric knowledge using a pre-calibrated confidence threshold to mitigate hallucination in fictional character role-play. |
| Outcome: | The proposed method reduces the factual accuracy of generated responses by 18% for adversarial questions and 44% in temporal hallucination for time-sensitive interviews. |