Primacy, anchoring, and order-dependence are architecturally necessary in autoregressive models due to causal masking constraints, with supporting evidence from theorems, LLM fits, and human experiments.
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A reinforcement learning model is ethically fine-tuned using aggregated feedback from LLMs embodying five moral principles via Belief Jensen-Shannon Divergence and Dempster-Shafer Theory.
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Bias by Necessity: Impossibility Theorems for Sequential Processing with Convergent AI and Human Validation
Primacy, anchoring, and order-dependence are architecturally necessary in autoregressive models due to causal masking constraints, with supporting evidence from theorems, LLM fits, and human experiments.
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Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making
A reinforcement learning model is ethically fine-tuned using aggregated feedback from LLMs embodying five moral principles via Belief Jensen-Shannon Divergence and Dempster-Shafer Theory.