SOTAVerified

Ethics

Papers

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Showing 251–275 of 832 papers

TitleStatusHype
Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Prompting—0
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective—0
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security—0
Just Because We Camp, Doesn't Mean We Should: The Ethics of Modelling Queer Voices—0
Deception Analysis with Artificial Intelligence: An Interdisciplinary Perspective—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
Is On-Device AI Broken and Exploitable? Assessing the Trust and Ethics in Small Language Models—0
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey—0
Ethical considerations of use of hold-out sets in clinical prediction model management—0
AI as Decision-Maker: Ethics and Risk Preferences of LLMs—0
Exploring and steering the moral compass of Large Language ModelsCode0
Generation of synthetic data using breast cancer dataset and classification with resnet18—0
Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models—0
Toward Constraint Compliant Goal Formulation and Planning—0
Quelle éthique pour quelle IA ?—0
Towards interactive evaluations for interaction harms in human-AI systems—0
Navigating LLM Ethics: Advancements, Challenges, and Future Directions—0
Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models—0
Large Language Model in Financial Regulatory Interpretation—0
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines—0
More RLHF, More Trust? On The Impact of Preference Alignment On TrustworthinessCode0
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them in—0
A Taxation Perspective for Fair Re-rankingCode0
Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing—0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law—0
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All datasetsETHICSEthics (per ethics)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RuGPT-3 LargeAccuracy68.6—Unverified
2RuGPT-3 MeduimAccuracy68.3—Unverified
3RuGPT-3 SmallAccuracy55.5—Unverified
4Human benchmarkAccuracy52.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy67.6—Unverified
2RuGPT-3 SmallAccuracy60.9—Unverified
3RuGPT-3 LargeAccuracy44.9—Unverified
4RuGPT-3 MediumAccuracy44.1—Unverified