S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information. - Jiaxin Pei; Chenliang Li (EMNLP, 2018)

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets. - Canwen Xu; Jing Li 034; Xiangyang Luo; Jiaxin Pei; Chenliang Li; Donghong Ji (WWW, 2019)

Targeted Sentiment Analysis: A Data-Driven Categorization. - Jiaxin Pei; Aixin Sun; Chenliang Li (CoRR, 2019)

SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction. - Qichen Li; Jiaxin Pei; Jianding Zhang; Bo Han (CoRR, 2019)

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders. - Yu Duan; Jiaxin Pei; Canwen Xu; Chenliang Li (CoRR, 2019)

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders. - Yu Duan; Canwen Xu; Jiaxin Pei; Jialong Han; Chenliang Li (ACL, 2020)

MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization. - Canwen Xu; Jiaxin Pei; Hongtao Wu; Yiyu Liu; Chenliang Li (ACL, 2020)

Quantifying Intimacy in Language. - Jiaxin Pei; David Jurgens (EMNLP (1), 2020)

Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications. - Jiaxin Pei; David Jurgens (EMNLP (1), 2021)

Modeling Information Change in Science Communication with Semantically Matched Paraphrases. - Dustin Wright 001; Jiaxin Pei; David Jurgens; Isabelle Augenstein (EMNLP, 2022)

POTATO: The Portable Text Annotation Tool. - Jiaxin Pei; Aparna Ananthasubramaniam; Xingyao Wang 002; Naitian Zhou; Apostolos Dedeloudis; Jackson Sargent; David Jurgens (EMNLP (Demos), 2022)

SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. - Jiaxin Pei; Vítor Silva 003; Maarten W. Bos; Yozon Liu; Leonardo Neves; David Jurgens; Francesco Barbieri (CoRR, 2022)

A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale. - Kyrie Zhixuan Zhou; Jiao Sun; Jiaxin Pei; Nanyun Peng 001; Jinjun Xiong (CoRR, 2022)

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research. - Dimosthenis Antypas; Asahi Ushio; Francesco Barbieri; Leonardo Neves; Kiamehr Rezaee; Luis Espinosa Anke; Jiaxin Pei; José Camacho-Collados (EMNLP (Findings), 2023)

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark. - Minje Choi; Jiaxin Pei; Sagar Kumar; Chang Shu; David Jurgens (EMNLP, 2023)

SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. - Jiaxin Pei; Vítor Silva 003; Maarten W. Bos; Yozen Liu; Leonardo Neves; David Jurgens; Francesco Barbieri (SemEval@ACL, 2023)

When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset. - Jiaxin Pei; David Jurgens (CoRR, 2023)

Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics. - Aparna Ananthasubramaniam; Hong Chen; Jason Yan; Kenan Alkiek; Jiaxin Pei; Agrima Seth; Lavinia Dunagan; Minje Choi; Benjamin Litterer; David Jurgens (CoRR, 2023)

Aligning with Whom? Large Language Models Have Gender and Racial Biases in Subjective NLP Tasks. - Huaman Sun; Jiaxin Pei; Minje Choi; David Jurgens (CoRR, 2023)

Is "A Helpful Assistant" the Best Role for Large Language Models? A Systematic Evaluation of Social Roles in System Prompts. - Mingqian Zheng; Jiaxin Pei; David Jurgens (CoRR, 2023)

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models. - Mingqian Zheng; Jiaxin Pei; Lajanugen Logeswaran; Moontae Lee; David Jurgens (EMNLP (Findings), 2024)

Modeling and Detecting Company Risks from News. - Jiaxin Pei; Soumya Vadlamannati; Liang-Kang Huang; Daniel Preotiuc-Pietro; Xinyu Hua (NAACL (Industry Track), 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions. - Matthias Orlikowski; Jiaxin Pei; Paul Röttger; Philipp Cimiano; David Jurgens; Dirk Hovy (ACL (1), 2025)

User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions. - Xianzhe Fan; Qing Xiao 002; Xuhui Zhou; Jiaxin Pei; Maarten Sap; Zhicong Lu; Hong Shen 004 (CHI, 2025)

Who Reaps All the Superchats? A Large-Scale Analysis of Income Inequality in Virtual YouTuber Livestreaming. - Ruijing Zhao; Brian Diep; Jiaxin Pei; Dongwook Yoon; David Jurgens; Jian Zhu (CHI, 2025)

Sociodemographic Prompting is Not Yet an Effective Approach for Simulating Subjective Judgments with LLMs. - Huaman Sun; Jiaxin Pei; Minje Choi; David Jurgens (NAACL (Short Papers), 2025)

Causally Modeling the Linguistic and Social Factors that Predict Email Response. - Yinuo Xu; Hong Chen; Sushrita Rakshit; Aparna Ananthasubramaniam; Omkar Yadav; Mingqian Zheng; Michael Jiang; Lechen Zhang; Bowen Yi; Kenan Alkiek; Abraham Israeli; Bangzhao Shu; et al. (NAACL (Long Papers), 2025)

Hidden Division of Labor in Scientific Teams Revealed Through 1.6 Million LaTeX Files. - Jiaxin Pei; Lulin Yang; Lingfei Wu 002 (CoRR, 2025)

Writing Patterns Reveal a Hidden Division of Labor in Scientific Teams. - Lulin Yang; Jiaxin Pei; Lingfei Wu 002 (CoRR, 2025)

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets. - Shenzhe Zhu; Jiao Sun; Yi Nian; Tobin South; Alex Pentland; Jiaxin Pei (CoRR, 2025)

Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce. - Yijia Shao; Humishka Zope; Yucheng Jiang; Jiaxin Pei; David Nguyen; Erik Brynjolfsson; Diyi Yang (CoRR, 2025)

Modeling Public Perceptions of Science in Media. - Jiaxin Pei; Dustin Wright 001; Isabelle Augenstein; David Jurgens (CoRR, 2025)