Verbalized Rejection Sampling reduces bias in LLM Bernoulli sampling by prompting the model to reason about and accept or reject proposed samples.
Evaluating binary decision biases in large language models: Implications for fair agent-based financial simulations
2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2verdicts
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LLMs deviate from human moral preferences in kidney allocation scenarios and rarely express indecision, though low-rank fine-tuning with few examples can improve both consistency and uncertainty calibration.
citing papers explorer
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Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling
Verbalized Rejection Sampling reduces bias in LLM Bernoulli sampling by prompting the model to reason about and accept or reject proposed samples.
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Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
LLMs deviate from human moral preferences in kidney allocation scenarios and rarely express indecision, though low-rank fine-tuning with few examples can improve both consistency and uncertainty calibration.