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Explore the challenges AI faces in interpreting human laws and regulations. Gain insights into open-texturedness and its impact on normative rule systems for agent-level AI development.
Discover how large language models assist social work experts in analyzing millions of Twitter posts about homelessness through OATH-Frames, revealing crucial public attitude trends and policy insights.
Explore groundbreaking research comparing LLMs with expert researchers in generating novel ideas, examining the potential and limitations of AI in accelerating scientific discovery through empirical evaluation.
Explore how larger language models handle subjective tasks, examining their limitations in emotion and morality recognition despite advanced prompting techniques and increased model sizes.
Explore the limitations and potential of Large Language Models in time-series analysis, examining their effectiveness compared to traditional methods for applications in finance and healthcare.
Explore cutting-edge techniques for training adaptable LLM agents through weakly supervised learning, focusing on frameworks like Agent Lumos and innovative approaches to enhance generalization capabilities.
Explore how language models exhibit covert racism through dialect prejudice, focusing on stereotypes about African American English and their implications for AI development and social impact.
Explore groundbreaking research challenging assumptions about language models' ability to learn impossible languages through systematic experiments with synthetic language variations and GPT-2.
Explore the importance of AI trustworthiness and source monitoring, and learn about knowledge graphs, AI assistants, and potential impacts on critical thinking.
Explore groundbreaking research on developing socially intelligent and safe AI systems, focusing on LLM evaluation frameworks, information asymmetry challenges, and safety considerations in human-AI interactions.
Explore the dual nature of bias in Large Language Models through research examining community perspective alignment and ideological vulnerabilities, with insights for responsible AI development.
Explore how content moderation systems and language models exhibit biases when detecting harmful speech in gender-queer dialects, with focus on reclaimed LGBTQ+ terminology and fairness implications.
Explore how Bayesian neural networks can enhance engineering predictive models by integrating physics-based knowledge and providing robust uncertainty quantification for high-consequence decision-making applications.
Explore cybersecurity challenges with David Balenson as he discusses autonomous vehicle safety, AI lessons from cybersecurity, and the paradox of our technological dependence versus resilience.
Explore critical issues in NLP practices, examining the misuse of "democratization" and ethical problems with associating personal names with sociodemographic attributes, while offering recommendations for advancing justice in the field.
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