用弱指令训练模型，让故事生成更懂细节、更连贯。
Instruction Tuning for Story Understanding and Generation with Weak Supervision
- 通过弱到强的指令渐进式微调，提升模型理解力。
- 在多个数据集上超越现有方法，人评与自动指标双优。
- 适合需要精准叙事能力的AI写作场景。
故事理解与生成是自然语言处理中的长期挑战，尤其在不同指令清晰度下表现各异。本文提出一种新方法——‘弱到强指令微调’，通过不同清晰度的指令对大语言模型进行微调，以增强其故事生成能力。我们探索了大模型对弱指令与强指令的适应性，结果表明该方法显著提升了模型在故事情节、人物和主题理解上的表现，并生成连贯吸引人的叙事。在多个基准数据集上进行大量实验并与当前最优方法对比，结果显示本方法在自动评估指标和人工评价中均有显著提升。研究证明，自适应指令微调可成为优化生成模型完成复杂叙事任务的强大工具。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。