U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
arXiv preprint arXiv:2406.18841 (2024)
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Across 43,200 simulations with five LLMs and five scenarios, model trust in humans aligns with human-like patterns driven by trustworthiness dimensions and is sometimes biased by age, gender, and religion.
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.
A multi-dimensional audit framework for politically aligned LLMs finds consistent trade-offs: larger models are more effective and truthful but less fair with higher bias, while fine-tuned models reduce bias but increase hallucinations and reasoning decline, and all tested models show deficiencies.
citing papers explorer
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U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning
U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
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A closer look at how large language models trust humans: patterns and biases
Across 43,200 simulations with five LLMs and five scenarios, model trust in humans aligns with human-like patterns driven by trustworthiness dimensions and is sometimes biased by age, gender, and religion.
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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.
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A Multi-Dimensional Audit of Politically Aligned Large Language Models
A multi-dimensional audit framework for politically aligned LLMs finds consistent trade-offs: larger models are more effective and truthful but less fair with higher bias, while fine-tuned models reduce bias but increase hallucinations and reasoning decline, and all tested models show deficiencies.