Papers by Bowen Yi

Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)

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Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin

Challenge:	Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results .
Approach:	They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method .
Outcome:	The proposed methods outperform random selection on large datasets on large data pools.

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Green & Sustainable NLP

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Reasoning

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)

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Yi Su, Yunpeng Tai, Yixin Ji, Juntao Li, Yan Bowen, Min Zhang

Low-Resource NLP

Challenge:	Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates.
Approach:	They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance.
Outcome:	The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time.

The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models (2024.emnlp-main)

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Siyang Liu, Trisha Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea

Semantic Text Processing

Challenge:	Using the World Value Survey, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population.
Approach:	They use data from the World Value Survey to examine the alignment of LLM values with specific age groups.
Outcome:	The proposed model can be used to predict the value of a large language model and to assess its performance on 13 categories.

Domain Incremental Lifelong Learning in an Open World (2023.findings-acl)

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Yi Dai, Hao Lang, Yinhe Zheng, Bowen Yu, Fei Huang, Yongbin Li

Semantic Text Processing

Challenge:	Existing approaches to lifelong learning (LL) models require access to task identities in the testing phase or cannot handle samples from unseen tasks.
Approach:	They propose a dynamic architecture-based lifelong learning model that tries to learn a sequence of tasks with a prompt-enhanced language model.
Outcome:	The proposed model outperforms state-of-the-art models in handling unseen tasks and focuses on task-level prompts to capture knowledge from different granularities.

Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)

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Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang, Chang Zhou

Language Models

Challenge:	Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment.
Approach:	They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs.
Outcome:	The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger.

Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)

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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens

Dialogue Systems & Conversational Agents

Challenge:	Recent work has sought to use large language models to simulate human-human and human-LLM interactions.
Approach:	They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts.
Outcome:	The proposed models perform similarly in simulating English, Chinese, and Russian dialogues.

Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish (2025.naacl-short)

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Aylin Ece Gunal, Bowen Yi, John D. Piette, Rada Mihalcea, Veronica Perez-Rosas

Challenge:	Cultural and language factors influence counseling, but research has not explored whether this applies to other languages.
Approach:	They introduce a Spanish-language counseling dataset that contains expert annotations for counseling reflections and questions.
Outcome:	The proposed dataset explores language-based differences in counselor behavior in English and Spanish and develops classifiers in monolingual and multilingual settings.

NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)

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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva

Responsible & Trustworthy NLP

Challenge:	This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.
Approach:	This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.
Outcome:	The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas.

Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)

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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens

Challenge:	a key intent behind many emails is to get a reply from the recipient.
Approach:	They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations.
Outcome:	The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates .

SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents (2025.emnlp-main)

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Bowen Zhang, Yi Yang, Fuqiang Niu, Xianghua Fu, Genan Dai, Hu Huang

Explainability & Interpretability in NLP

Challenge:	a new framework for topic evolution and stance dynamics is needed to understand online discourse . topic evolution is central to understanding fragmentation of debates, spread of misinformation .
Approach:	They propose a stance and topic evolution reasoning framework for co-evolution of topics and stances through natural language interactions.
Outcome:	The proposed framework captures key empirical patterns across five real-world domains.

Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)

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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou

Low-Resource NLP

Challenge:	Existing methods for few-shot learning are insufficient to capture task variations in natural language domains.
Approach:	They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task.
Outcome:	The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets.