小模型也能生成高质量故事，靠的是分步优化与智能评估。
PlotTwist: A Creative Plot Generation Framework with Small Language Models
- 用三个模块分工协作：评分模型、专家混合生成器、独立评估系统。
- 30亿参数小模型在多个维度上击败大模型，胜率超90%。
- 适合想低成本做创意叙事的开发者和内容创作者。
创意情节生成对语言模型构成根本挑战：如何将简短前提转化为保持全局连贯性、角色发展、节奏、语调一致性和情感推进的完整叙事。尽管近期大语言模型（LLMs）在通用任务上表现流畅，但要在创意情节生成等特定领域表现优异，需进行偏好对齐，而这一过程在前沿大模型规模下计算成本极高，严重限制了可及性与实际部署。为此，我们提出PlotTwist，一种结构化框架，使参数量不超过30亿的轻量级语言模型（SLMs）能够生成质量媲美大规模前沿系统的前提引导型情节。该方法将生成分解为三个专用组件：(1) 通过新型正负提示策略训练的方面评分奖励模型；(2) 基于直接偏好优化（DPO）对齐的专家混合（MoE）情节生成器；(3) 使用跨家族评审团的代理评估模块，实现无偏、独立的事后评估。大量实验表明，PlotTwist在多个叙事质量维度（NQDs）上持续超越所有基线，包括前沿模型，除最强基线外胜率均高于其他模型，且与之保持竞争。进一步验证显示，该框架对叙事质量具有强敏感性，能可靠区分来自获奖与口碑差剧本的情节。这些结果确立了结构化、基于偏好的对齐是高效生成高质量创意情节的有效路径。
原文摘要 · Abstract (English)
Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains global coherence, character development, pacing, tone consistency, and emotional progression. Although recent Large Language Models (LLMs) demonstrate strong fluency on general-purpose tasks, they require preference alignment to perform well on domain-specific tasks such as creative plot generation. However, conducting such alignment at the scale of frontier LLMs is computationally prohibitive, significantly limiting accessibility and practical deployment. To address this, we present PlotTwist, a structured framework that enables Small Language Models (SLMs) with $\leq$3B active parameters to generate high-quality, premise-conditioned plots competitive with frontier systems of vastly greater parameter scale. Our approach decomposes generation into three specialized components: (1) an Aspect Rating Reward Model, trained via a novel Positive-Negative prompting strategy; (2) a Mixture-of-Experts (MoE) plot generator aligned via Direct Preference Optimization (DPO); and (3) an Agentic Evaluation module using a cross-family jury for unbiased, independent post-hoc assessment. Extensive experiments demonstrate that PlotTwist consistently outperforms all baselines, including frontier models, across multiple Narrative Quality Dimensions (NQDs), achieving higher win rates against every baseline except the strongest, with which it remains competitive. Further validation confirms strong sensitivity to narrative quality, as the framework reliably distinguishes plots derived from critically acclaimed versus widely panned screenplays. Together, these results establish structured, preference-based alignment as a resource-efficient approach to high-quality creative plot generation. Project page: https://abhinavthorat.github.io/plottwist/
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