Hang Jiang, Nanxi Liu, Tiantian Wang, Cassandra Overney, Rukun Zhang, Artemisia Luk, Diyi Yang, Deb Roy 等 9 位作者
Given that the Chinese are one of the oldest ethnic groups to immigrate to the US, their cuisine have become central to the US dining scene and an interesting avenue to study how Chinese immigrants have culturally adapted into American life. In that regard, previous research has focused on analyzing the English names that Chinese restaurants adopt, e.g., finding the use of familiar and typical words. To investigate how Chinese immigrants have culturally integrated in the US depending on social, geographic and demographic contexts, we investigate in this work the under-explored relationship between the Chinese and English names of American Chinese restaurants. Specifically, we conduct a study on 3,172 Chinese restaurants (10% of the total on Google Maps) sampled across the US. We present 8-dimension name frames to capture the underlying meanings behind names and create the NameFrames dataset containing 884 annotated parallel Chinese/English names. We also built an NLP pipeline to analyze the semantic discrepancies between names with both frame-based and embedding-based distance metrics. Our frame analysis shows that English names more often emphasize Location and Specialty, while Chinese names more often emphasize Positivity and Ambiance. Furthermore, we observe that for Chinese restaurants located in urban areas, those in counties with a higher concentration of Chinese population, or those that are relatively new, the discrepancies between the English and Chinese names are noticeably larger. Finally, interviews with 10 Chinese restaurant owners reveal additional interesting insights regarding naming conventions.
Woody Haosheng Gan, William Barr Held OpenAthena, Diyi Yang Stanford University
The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate whether minimal subsets can reliably evaluate LAMs while reducing costs and data redundancy. Analyzing 10 subset selection methods with 18 audio models across 40 tasks covering major LAM evaluation dimensions, we show that subsets of just 50 examples (0.3% of data) can achieve over 0.93 Pearson correlation with full benchmark scores. To understand how well these scores align with what practitioners ultimately care about—user satisfaction—we collect 776 human preference ratings from realistic voice assistant conversations, finding that both subsets and full benchmark achieve only 0.85 correlation with human. To better predict preferences, we trained regression models on these selected subsets, achieving 0.98 correlation—outperforming regression models trained on both random subsets and the full benchmark. This demonstrates that in regression modeling, well-curated subsets outpredict the full benchmark, showing quality over quantity. We open-source these regression-weighted subsets as the HUMANS benchmark, an efficient proxy for LAM evaluation that captures both benchmark performance and user preferences.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zora Wang Carnegie Mellon University, Yijia Shao Stanford University, David Nguyen Stanford University, Diyi Yang Stanford University
The recent development of large language models (LLMs) has revolutionized the landscape of human work. These models possess the ability to follow complex human instructions and operate versatile computer software, enabling them to participate in, augment, or even automate realistic occupational tasks that once thought to be exclusive to humans. As LLMs are increasingly integrated into workplaces, they are already reshaping labor dynamics (Hoffmann et al., 2024; Demirci et al., 2025) and raising urgent concerns about job displacement, diminished human agency, and overreliance on automation (Hazra et al., 2025). As a result, the future of work is undergoing a profound transformation: How will human occupations and task requirements evolve over time? And what roles will LLM-based systems play, as they become increasingly capable collaborators and autonomous workers? And how can we build technological and data infrastructures to support human-AI collaboration? This tutorial will provide an overview of the future of work shaped by the interplay of LLMs and humans, examining the emerging challenges, opportunities, and ethical considerations in this dynamic landscape. We begin by examining the economic landscape of work and how NLP technologies drive automation, followed by methods for developing LLMs that augment human labor and recent advances in LLM-based agents. We then cover evaluation approaches for workplace contexts, including datasets, benchmarks, and metrics, and conclude with open questions on technical, human, and societal implications.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · 2026 · 正式发表