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pro vyhledávání: '"Perez-Ortiz A"'
Recent advancements in Large Language Models (LLMs) have positioned them as powerful tools for clinical decision-making, with rapidly expanding applications in healthcare. However, concerns about bias remain a significant challenge in the clinical im
Externí odkaz:
http://arxiv.org/abs/2410.16574
Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are incredibly complex, involving conflicting entities and evidence. In the last decades, policymakers increasing
Externí odkaz:
http://arxiv.org/abs/2410.07287
While large language models (LLMs) are increasingly playing a pivotal role in education by providing instantaneous, adaptive responses, their potential to promote critical thinking remains understudied. In this paper, we fill such a gap and present a
Externí odkaz:
http://arxiv.org/abs/2409.05511
Large Language Models (LLMs) have been increasingly used in real-world settings, yet their strategic decision-making abilities remain largely unexplored. To fully benefit from the potential of LLMs, it's essential to understand their ability to funct
Externí odkaz:
http://arxiv.org/abs/2407.04467
Autor:
Lim, Serene, Pérez-Ortiz, María
This paper investigates the subtle and often concealed biases present in Large Language Models (LLMs), focusing on implicit biases that may remain despite passing explicit bias tests. Implicit biases are significant because they influence the decisio
Externí odkaz:
http://arxiv.org/abs/2407.01270
Autor:
Wang, Ze, Wu, Zekun, Guan, Xin, Thaler, Michael, Koshiyama, Adriano, Lu, Skylar, Beepath, Sachin, Ertekin Jr., Ediz, Perez-Ortiz, Maria
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) for resu
Externí odkaz:
http://arxiv.org/abs/2406.15484
Large language models (LLMs) are becoming bigger to boost performance. However, little is known about how explainability is affected by this trend. This work explores LIME explanations for DeBERTaV3 models of four different sizes on natural language
Externí odkaz:
http://arxiv.org/abs/2405.05348
Autor:
Lou, Andrés, Pérez-Ortiz, Juan Antonio, Sánchez-Martínez, Felipe, Sánchez-Cartagena, Víctor M.
Publikováno v:
2024.naacl-long.156
The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure. In this paper we develop, curat
Externí odkaz:
http://arxiv.org/abs/2404.07673
In order to oversee advanced AI systems, it is important to understand their underlying decision-making process. When prompted, large language models (LLMs) can provide natural language explanations or reasoning traces that sound plausible and receiv
Externí odkaz:
http://arxiv.org/abs/2404.03189
Stereotype detection is a challenging and subjective task, as certain statements, such as "Black people like to play basketball," may not appear overtly toxic but still reinforce racial stereotypes. With the increasing prevalence of large language mo
Externí odkaz:
http://arxiv.org/abs/2404.01768