可控叙事渲染以增强辅助写作
Controllable Narrative Rendering for Enhanced Assisted Writing
AI总结：
针对大语言模型在创意写作中安全编辑与无控制情节扩展的二元失败问题，提出基于叙事学故事/话语区分的Loom框架，通过三层流水线和意图中心符号链实现叙事意图与渲染密度的精确控制，在保持事实完整性的同时提升描述强度。
摘要：
尽管大语言模型（LLM）在基础写作辅助方面表现出色，但它们在创意写作中的实用性受到持续二元失败的严重阻碍。这一问题表现为在安全的表面编辑（称为补救性润色）与破坏性的无控制情节扩展之间摇摆。这一困境定义了叙事保真度与描述强度之间的关键权衡。我们提出Loom，一个基于叙事学故事与话语区分的辅助写作框架。Loom采用三层流水线，通过以意图为中心的符号思维链实现对叙事意图和渲染密度的精确控制。该架构将感知材料的生成与句法插入分离，确保增强在不违反原始事件结构的情况下发生。我们的综合评估（包括基于LLM的指标和人工评估）表明，Loom成功解决了这一基本矛盾。Loom取得了最高的整体质量分数，与最先进的基线相比，在事实完整性和描述强度方面取得了显著提升。
英文摘要：
Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hindered by a persistent binary failure. This issue manifests as an oscillation between safe, surface-level editing, referred to as remedial polishing, and destructive, uncontrolled plot expansion. This dilemma defines a critical trade-off between narrative fidelity and descriptive intensity. We propose Loom, an assisted writing framework grounded in the narratological distinction between story and discourse. Loom employs a three-layer pipeline that operationalizes an intent-centered semiotic chain-of-thought to enforce precise control over narrative intent and rendering density. This architecture separates the generation of perceptual material from syntactic insertion, ensuring that enhancement occurs without violating the original event structure. Our comprehensive evaluation, which includes LLM-based metrics and human assessment, demonstrates that Loom successfully resolves this fundamental tension. Loom achieves the highest overall quality score, yielding substantial gains in factual integrity and descriptive intensity compared to state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
