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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.06901 |
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| _version_ | 1866910200179458048 |
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| author | Ziems, Caleb Zhao, Dora Wang, Rose E. Jörke, Matthew Rushdi, Ahmad Deepak, Advit Yu, Sunny Agarwal, Anshika Agarwal, Harshvardhan Aranguiz-Dias, Gabriela Bhagirath, Aditri Breuch, Justine Chen, Huanxing Chen, Ruishi Chen, Sarah Fan, Haocheng Fang, William Fergesen, Cat Gonzales Frees, Daniel Gao, Tian Huang, Ziqing Jain, Vishal Jiang, Yucheng Kalinin, Kirill Karaca, Su Doga Khatua, Arpandeep La, Teland Levent, Isabelle Li, Miranda Li, Xinling Li, Yongce Liu, Angela Oh, Minsik Paek, Nathan J. Qin, Anthony Redmond, Emily Ryan, Michael J. Salecha, Aadesh Shen, Xiaoxian Singhal, Pranava Subrahmanya, Shashanka Tan, Mei Thawornbut, Irawadee Vinocour, Michelle Wang, Xiaoyue Wang, Zheng Weng, Henry Jin Wirawarn, Pawan Wu, Shirley Wu, Sophie Xie, Yichen Ye, Patrick Zhang, Sean Zhang, Yutong Zhou, Cathy Zhao, Yiling Landay, James Yang, Diyi |
| author_facet | Ziems, Caleb Zhao, Dora Wang, Rose E. Jörke, Matthew Rushdi, Ahmad Deepak, Advit Yu, Sunny Agarwal, Anshika Agarwal, Harshvardhan Aranguiz-Dias, Gabriela Bhagirath, Aditri Breuch, Justine Chen, Huanxing Chen, Ruishi Chen, Sarah Fan, Haocheng Fang, William Fergesen, Cat Gonzales Frees, Daniel Gao, Tian Huang, Ziqing Jain, Vishal Jiang, Yucheng Kalinin, Kirill Karaca, Su Doga Khatua, Arpandeep La, Teland Levent, Isabelle Li, Miranda Li, Xinling Li, Yongce Liu, Angela Oh, Minsik Paek, Nathan J. Qin, Anthony Redmond, Emily Ryan, Michael J. Salecha, Aadesh Shen, Xiaoxian Singhal, Pranava Subrahmanya, Shashanka Tan, Mei Thawornbut, Irawadee Vinocour, Michelle Wang, Xiaoyue Wang, Zheng Weng, Henry Jin Wirawarn, Pawan Wu, Shirley Wu, Sophie Xie, Yichen Ye, Patrick Zhang, Sean Zhang, Yutong Zhou, Cathy Zhao, Yiling Landay, James Yang, Diyi |
| contents | Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and science. With this rise in global influence comes greater urgency to build, evaluate, and deploy these systems in a manner that prioritizes not only technical capabilities but also human priorities. This work presents a framework for developing Human-Centered Large Language Models (HCLLMs), which integrates perspectives from Natural Language Processing (NLP), Human-Computer Interaction (HCI), and responsible AI. Considering the ethics, economics, and technical objectives of language modeling, we argue that model developers need to address human concerns, preferences, values, and goals, not only during a cursory post-training stage, but rather with rigor and care at every stage of the pipeline. This paper offers human-centered insights and recommendations for developers at each stage, from system design to data sourcing, model training, evaluation, and responsible deployment. Then we conclude with a case study, applying these insights to understand the future of work with HCLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06901 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reflections and New Directions for Human-Centered Large Language Models Ziems, Caleb Zhao, Dora Wang, Rose E. Jörke, Matthew Rushdi, Ahmad Deepak, Advit Yu, Sunny Agarwal, Anshika Agarwal, Harshvardhan Aranguiz-Dias, Gabriela Bhagirath, Aditri Breuch, Justine Chen, Huanxing Chen, Ruishi Chen, Sarah Fan, Haocheng Fang, William Fergesen, Cat Gonzales Frees, Daniel Gao, Tian Huang, Ziqing Jain, Vishal Jiang, Yucheng Kalinin, Kirill Karaca, Su Doga Khatua, Arpandeep La, Teland Levent, Isabelle Li, Miranda Li, Xinling Li, Yongce Liu, Angela Oh, Minsik Paek, Nathan J. Qin, Anthony Redmond, Emily Ryan, Michael J. Salecha, Aadesh Shen, Xiaoxian Singhal, Pranava Subrahmanya, Shashanka Tan, Mei Thawornbut, Irawadee Vinocour, Michelle Wang, Xiaoyue Wang, Zheng Weng, Henry Jin Wirawarn, Pawan Wu, Shirley Wu, Sophie Xie, Yichen Ye, Patrick Zhang, Sean Zhang, Yutong Zhou, Cathy Zhao, Yiling Landay, James Yang, Diyi Computation and Language Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and science. With this rise in global influence comes greater urgency to build, evaluate, and deploy these systems in a manner that prioritizes not only technical capabilities but also human priorities. This work presents a framework for developing Human-Centered Large Language Models (HCLLMs), which integrates perspectives from Natural Language Processing (NLP), Human-Computer Interaction (HCI), and responsible AI. Considering the ethics, economics, and technical objectives of language modeling, we argue that model developers need to address human concerns, preferences, values, and goals, not only during a cursory post-training stage, but rather with rigor and care at every stage of the pipeline. This paper offers human-centered insights and recommendations for developers at each stage, from system design to data sourcing, model training, evaluation, and responsible deployment. Then we conclude with a case study, applying these insights to understand the future of work with HCLLMs. |
| title | Reflections and New Directions for Human-Centered Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.06901 |