让大模型一次性生成多个回答，能显著提升多样性。
Asking a Language Model for Diverse Responses
- 用一次生成n个候选答案，比逐个生成更高效
- 在相同计算开销下，多样性提升明显
- 适合需要多角度回复的对话系统
大型语言模型越来越依赖显式推理链，并能为同一上下文生成多个合理回应。本文研究了生成候选回应的采样策略，对比了传统的并行采样与两种替代方法：枚举法（在一次推理中生成n个候选）和迭代采样法（依次生成，基于已生成回应集进行条件化）。在相同预算下，我们从质量、词汇多样性和计算流程多样性以及效率四个维度进行了比较。实验结果表明，在保持相当生成质量的前提下，枚举法和迭代采样法均能显著提升响应多样性。研究结果凸显了简单非独立采样策略在不牺牲生成质量的情况下增强响应多样性的潜力。
原文摘要 · Abstract (English)
Large language models increasingly rely on explicit reasoning chains and can produce multiple plausible responses for a given context. We study the candidate sampler that produces the set of plausible responses contrasting the ancestral (parallel) sampling against two alternatives: enumeration, which asks the model to produce $n$ candidates in one pass, and iterative sampling, which proposes candidates sequentially while conditioning on the currently generated response set. Under matched budgets, we compare these samplers on quality, lexical and computation flow diversity, and efficiency. Our empirical results demonstrate that enumeration and iterative strategies result in higher diversity at comparable quality. Our findings highlight the potential of simple non-independent sampling strategies to improve response diversity without sacrificing generation quality.
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