arXiv AI August 28, 2026 By Zihao Guo, Hongtao Lv, Chaoli Zhang, Laiguo Yin, Lei Liu, Yonghui Xu, Lizhen Cui

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

The paper introduces Dual-Seed Comparison (DSC), a protocol that uses two independent LLM-generated seeds to reduce systematic bias in probabilistic sampling. DSC constructs a bit sequence from the character-level ordinal values of the seeds, normalizes it into a pseudo-uniform variate, and maps it to the target distribution via the inverse cumulative distribution function. Empirical results show DSC outperforms existing methods in 96% of evaluated settings and enhances distributional control in tasks like MCQ generation and attribute-constrained text-to-image prompting.

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