通过回答集编程引导和多样化基于LLM的故事生成
Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming
AI总结：
本文提出利用回答集编程（ASP）作为高层符号规范，引导和多样化LLM故事生成，实验证明该方法比未引导的LLM生成更多样化的故事，并提升了大纲生成的紧凑性和灵活性。
摘要：
指令调优的大语言模型（LLMs）能够响应开放式用户请求生成故事，但由此产生的故事往往在多样性方面受限。较老的符号化故事生成方法（如规划）可以生成显著更多样化的情节大纲，但仅限于生成重新组合一组固定的手工设计角色动作模板的故事。我们能否在结合这些方法优势的同时减轻其弱点？我们提出通过使用更高级、更抽象的符号化规范来定义高层故事结构——通过回答集编程（ASP）实现——以引导和多样化基于LLM的故事生成。通过语义相似性分析，我们证明我们的方法比未引导的LLM生成更多样化的故事，并通过代码片段，我们展示了基于ASP的大纲生成相对于完整叙事规划在紧凑性和灵活性方面的改进。
英文摘要：
Instruction-tuned large language models (LLMs) are capable of generating stories in response to open-ended user requests, but the resulting stories tend to be limited in their diversity. Older, symbolic approaches to story generation (such as planning) can generate substantially more diverse plot outlines, but are limited to producing stories that recombine a fixed set of hand-engineered character action templates. Can we combine the strengths of these approaches while mitigating their weaknesses? We propose to do so by using a higher-level and more abstract symbolic specification of high-level story structure -- implemented via answer set programming (ASP) -- to guide and diversify LLM-based story generation. Via semantic similarity analysis, we demonstrate that our approach produces more diverse stories than an unguided LLM, and via code excerpts, we demonstrate the improved compactness and flexibility of ASP-based outline generation over full-fledged narrative planning.
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。
