A Method for Learning Value Systems in Generative AI | Paper Citation Record

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A Method for Learning Value Systems in Generative AI The 9th International Workshop on Freight Transportation and Logistics (ODYSSEUS) , title =

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A Method for Learning Value Systems in Generative AI XIII International Workshop on Locational Analysis and Related Problems (IWOLOCA) , title =

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A Method for Learning Value Systems in Generative AI Vehicle Routing Problem with Fair Profits and Time Windows (

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A Method for Learning Value Systems in Generative AI CEUR Proceedings, 12th Int

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A Method for Learning Value Systems in Generative AI 2024 , journal =

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A Method for Learning Value Systems in Generative AI 2024 , month = oct, journal =

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A Method for Learning Value Systems in Generative AI Multi-Objective Deep Inverse Reinforcement Learning through Direct Weights and Rewards Estimation , year=

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A Method for Learning Value Systems in Generative AI Comparison of distance and reinforcement-learning rules in social-influence models , volume =

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A Method for Learning Value Systems in Generative AI Bezdek and Robert Ehrlich and William Full , keywords =

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A Method for Learning Value Systems in Generative AI 2017 , booktitle =

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A Method for Learning Value Systems in Generative AI Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,

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A Method for Learning Value Systems in Generative AI Zhang and Tengfei Liu and Leonard K.M

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A Method for Learning Value Systems in Generative AI , title =

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A Method for Learning Value Systems in Generative AI and Cortinhal, Maria Jo \ a o

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A Method for Learning Value Systems in Generative AI Multi-Criteria Recommender Systems , url =

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A Method for Learning Value Systems in Generative AI Predicting users' movie preference and rating behavior from personality and values , volume =

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A Method for Learning Value Systems in Generative AI Supervised clustering of label ranking data using label preference information , volume =

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A Method for Learning Value Systems in Generative AI and Sucholutsky, Ilia and Griffiths, Thomas L

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A Method for Learning Value Systems in Generative AI Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

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A Method for Learning Value Systems in Generative AI S im CSE : Simple Contrastive Learning of Sentence Embeddings

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A Method for Learning Value Systems in Generative AI Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization

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A Method for Learning Value Systems in Generative AI Proceedings of the 41st International Conference on Machine Learning , articleno =

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A Method for Learning Value Systems in Generative AI Artificial Intelligence and the Problem of Control

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A Method for Learning Value Systems in Generative AI Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , author=

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A Method for Learning Value Systems in Generative AI Modular Pluralism: Pluralistic Alignment via Multi- LLM Collaboration

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A Method for Learning Value Systems in Generative AI The Value Learning Problem , year =

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A Method for Learning Value Systems in Generative AI Moral Values in Norm Decision Making , year =

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A Method for Learning Value Systems in Generative AI Artificial Intelligence, Values, and Alignment , volume =

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A Method for Learning Value Systems in Generative AI Value Engineering for Autonomous Agents

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A Method for Learning Value Systems in Generative AI Transparent Value Alignment , year =

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A Method for Learning Value Systems in Generative AI An Efficient, Generalized

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A Method for Learning Value Systems in Generative AI Algorithmic bias and the value sensitive design approach , volume =

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A Method for Learning Value Systems in Generative AI Proceedings of the 30th International Conference on Neural Information Processing Systems , pages =

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A Method for Learning Value Systems in Generative AI User Study Design for Identifying the Semantics of Bioethical Principles

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Reference 86

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Reference 89

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Reference 90

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Reference 91

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Reference 92

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A Method for Learning Value Systems in Generative AI Proceedings of the 37th International Conference on Machine Learning , pages =

Reference 93

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Reference 100

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