基于弱监督的故事理解与生成的指令微调
Instruction Tuning for Story Understanding and Generation with Weak Supervision
AI总结：
提出“Weak to Strong Instruction Tuning”方法，通过使用不同清晰度的指令微调LLMs，显著提升了故事理解与生成的性能，在自动和人工评估中均优于现有技术。
摘要：
故事理解与生成一直是自然语言处理（NLP）中的一项具有挑战性的任务，特别是在处理不同水平的指令明确性时。在本文中，我们提出了一种名为“Weak to Strong Instruction Tuning”的新颖方法，通过使用不同清晰度的指令微调模型来改进故事生成。我们探索了大型语言模型（LLMs）适应不同类型指令（弱指令和强指令）的潜力，并表明我们的方法显著增强了故事理解与生成的性能。通过利用指令微调的优势，我们训练模型理解故事情节、角色和主题的细微差别，同时生成连贯且引人入胜的叙事。通过对几个基准数据集进行广泛实验，并与最先进的基线进行比较，我们证明我们的方法优于现有技术，在自动评估指标和人工评估中均取得了实质性改进。我们的工作表明，自适应指令微调可以成为优化复杂叙事任务的生成模型的强大工具。
英文摘要：
Story understanding and generation have long been a challenging task in natural language processing (NLP), especially when dealing with various levels of instruction specificity. In this paper, we propose a novel approach called "Weak to Strong Instruction Tuning" for improving story generation by tuning models with instructions of varying clarity. We explore the potential of large language models (LLMs) to adapt to different types of instructions, weak and strong, and show that our method significantly enhances performance in story comprehension and generation. By leveraging the strength of instruction tuning, we train models to understand the nuances of story plots, characters, and themes while generating coherent and engaging narratives. Through extensive experiments on several benchmark datasets and comparison with state-of-the-art baselines, we demonstrate that our method outperforms existing techniques, yielding substantial improvements in both automatic evaluation metrics and human evaluations. Our work shows that adaptive instruction tuning can be a powerful tool in refining generative models for complex narrative tasks.
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。
