Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Card, Dallas"'
Publikováno v:
Journal of Quantitative Description: Digital Media (2024)
Social media enables activists to directly communicate with the public and provides a space for movement leaders, participants, bystanders, and opponents to collectively construct and contest narratives. Focusing on Twitter messages from social movem
Externí odkaz:
http://arxiv.org/abs/2406.13820
Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pai
Externí odkaz:
http://arxiv.org/abs/2312.02118
Autor:
Shu, Bangzhao, Zhang, Lechen, Choi, Minje, Dunagan, Lavinia, Logeswaran, Lajanugen, Lee, Moontae, Card, Dallas, Jurgens, David
The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. To properly understand the properties and innate personas of LLMs, researchers have performed studies that
Externí odkaz:
http://arxiv.org/abs/2311.09718
Autor:
Card, Dallas
Publikováno v:
In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2023
Measuring semantic change has thus far remained a task where methods using contextual embeddings have struggled to improve upon simpler techniques relying only on static word vectors. Moreover, many of the previously proposed approaches suffer from d
Externí odkaz:
http://arxiv.org/abs/2309.02403
Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore). Here, we uncover systematic ways in which word similarities estimated by cosine over BERT embeddings are understated and trace this
Externí odkaz:
http://arxiv.org/abs/2205.05092
Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment. However, without access to source data it is difficult to account for domain shift, which represents a threat to validi
Externí odkaz:
http://arxiv.org/abs/2204.14213
Autor:
Gururangan, Suchin, Card, Dallas, Dreier, Sarah K., Gade, Emily K., Wang, Leroy Z., Wang, Zeyu, Zettlemoyer, Luke, Smith, Noah A.
Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text
Externí odkaz:
http://arxiv.org/abs/2201.10474
Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performan
Externí odkaz:
http://arxiv.org/abs/2110.00613
Autor:
Bommasani, Rishi, Hudson, Drew A., Adeli, Ehsan, Altman, Russ, Arora, Simran, von Arx, Sydney, Bernstein, Michael S., Bohg, Jeannette, Bosselut, Antoine, Brunskill, Emma, Brynjolfsson, Erik, Buch, Shyamal, Card, Dallas, Castellon, Rodrigo, Chatterji, Niladri, Chen, Annie, Creel, Kathleen, Davis, Jared Quincy, Demszky, Dora, Donahue, Chris, Doumbouya, Moussa, Durmus, Esin, Ermon, Stefano, Etchemendy, John, Ethayarajh, Kawin, Fei-Fei, Li, Finn, Chelsea, Gale, Trevor, Gillespie, Lauren, Goel, Karan, Goodman, Noah, Grossman, Shelby, Guha, Neel, Hashimoto, Tatsunori, Henderson, Peter, Hewitt, John, Ho, Daniel E., Hong, Jenny, Hsu, Kyle, Huang, Jing, Icard, Thomas, Jain, Saahil, Jurafsky, Dan, Kalluri, Pratyusha, Karamcheti, Siddharth, Keeling, Geoff, Khani, Fereshte, Khattab, Omar, Koh, Pang Wei, Krass, Mark, Krishna, Ranjay, Kuditipudi, Rohith, Kumar, Ananya, Ladhak, Faisal, Lee, Mina, Lee, Tony, Leskovec, Jure, Levent, Isabelle, Li, Xiang Lisa, Li, Xuechen, Ma, Tengyu, Malik, Ali, Manning, Christopher D., Mirchandani, Suvir, Mitchell, Eric, Munyikwa, Zanele, Nair, Suraj, Narayan, Avanika, Narayanan, Deepak, Newman, Ben, Nie, Allen, Niebles, Juan Carlos, Nilforoshan, Hamed, Nyarko, Julian, Ogut, Giray, Orr, Laurel, Papadimitriou, Isabel, Park, Joon Sung, Piech, Chris, Portelance, Eva, Potts, Christopher, Raghunathan, Aditi, Reich, Rob, Ren, Hongyu, Rong, Frieda, Roohani, Yusuf, Ruiz, Camilo, Ryan, Jack, Ré, Christopher, Sadigh, Dorsa, Sagawa, Shiori, Santhanam, Keshav, Shih, Andy, Srinivasan, Krishnan, Tamkin, Alex, Taori, Rohan, Thomas, Armin W., Tramèr, Florian, Wang, Rose E., Wang, William, Wu, Bohan, Wu, Jiajun, Wu, Yuhuai, Xie, Sang Michael, Yasunaga, Michihiro, You, Jiaxuan, Zaharia, Matei, Zhang, Michael, Zhang, Tianyi, Zhang, Xikun, Zhang, Yuhui, Zheng, Lucia, Zhou, Kaitlyn, Liang, Percy
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically
Externí odkaz:
http://arxiv.org/abs/2108.07258
Autor:
Birhane, Abeba, Kalluri, Pratyusha, Card, Dallas, Agnew, William, Dotan, Ravit, Bao, Michelle
Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally be
Externí odkaz:
http://arxiv.org/abs/2106.15590