Zobrazeno 1 - 10
of 823
pro vyhledávání: '"Schmer A"'
Autor:
Friedman, Scott E., Benkler, Noam, Mosaphir, Drisana, Rye, Jeffrey, Schmer-Galunder, Sonja M., Goldwater, Micah, McLure, Matthew, Wheelock, Ruta, Gottlieb, Jeremy, Goldman, Robert P., Miller, Christopher
Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the
Externí odkaz:
http://arxiv.org/abs/2411.05040
Autor:
Martinez, Manuel Nunez, Schmer-Galunder, Sonja, Liu, Zoey, Youm, Sangpil, Jayaweera, Chathuri, Dorr, Bonnie J.
The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecti
Externí odkaz:
http://arxiv.org/abs/2411.04328
Autor:
Smart, Andrew, Hutchinson, Ben, Amugongo, Lameck Mbangula, Dikker, Suzanne, Zito, Alex, Ebinama, Amber, Wudiri, Zara, Wang, Ding, van Liemt, Erin, Sedoc, João, Olojo, Seyi, Uwakwe, Stanley, Wornyo, Edem, Schmer-Galunder, Sonja, Smith-Loud, Jamila
Publikováno v:
ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 2024
Large Language Models (LLMs) have rapidly increased in size and apparent capabilities in the last three years, but their training data is largely English text. There is growing interest in multilingual LLMs, and various efforts are striving for model
Externí odkaz:
http://arxiv.org/abs/2409.05247
Autor:
Schmer-Galunder, Sonja, Wheelock, Ruta, Friedman, Scott, Chvasta, Alyssa, Jalan, Zaria, Saltz, Emily
With the growing prevalence of large language models, it is increasingly common to annotate datasets for machine learning using pools of crowd raters. However, these raters often work in isolation as individual crowdworkers. In this work, we regard a
Externí odkaz:
http://arxiv.org/abs/2408.00880
Autor:
Smart, Andrew, Wang, Ding, Monk, Ellis, Díaz, Mark, Kasirzadeh, Atoosa, Van Liemt, Erin, Schmer-Galunder, Sonja
Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of research have begun to examine th
Externí odkaz:
http://arxiv.org/abs/2402.06811
The fields of AI current lacks methods to quantitatively assess and potentially alter the moral values inherent in the output of large language models (LLMs). However, decades of social science research has developed and refined widely-accepted moral
Externí odkaz:
http://arxiv.org/abs/2312.10075
Autor:
Víchová, Bronislava a, ⁎, Stanko, Michal a, Miterpáková, Martina a, Hurníková, Zuzana a, Syrota, Yaroslav a, c, Schmer-Jakšová, Patrícia a, Komorová, Petronela a, Vargová, Lucia a, Blažeková, Veronika a, Zubriková, Dana a, Švirlochová, Klaudia Mária a, d, Chovancová, Gabriela b
Publikováno v:
In Current Research in Parasitology & Vector-Borne Diseases 2025 7
Autor:
Mather, Brodie, Dorr, Bonnie J, Dalton, Adam, de Beaumont, William, Rambow, Owen, Schmer-Galunder, Sonja M.
We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endors
Externí odkaz:
http://arxiv.org/abs/2203.10659
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions betwee
Externí odkaz:
http://arxiv.org/abs/2202.11768
Autor:
Bybee-Finley, K. Ann, Muller, Katherine, White, Kathryn E., Cavigelli, Michel A., Han, Eunjin, Schomberg, Harry H., Snapp, Sieglinde, Viens, Frederi, Correndo, Adrian A., Deiss, Leonardo, Fonteyne, Simon, Garcia y Garcia, Axel, Gaudin, Amélie C.M., Hooker, David C., Janovicek, Ken, Jin, Virginia, Johnson, Gregg, Karsten, Heather, Liebman, Matt, McDaniel, Marshall D., Sanford, Gregg, Schmer, Marty R., Strock, Jeffrey, Sykes, Virginia R., Verhulst, Nele, Wilke, Brook, Bowles, Timothy M.
Publikováno v:
In One Earth 20 September 2024 7(9):1638-1654