In this study, we introduce AmbigNLG, a new task designed to tackle the challenge of task ambiguity in instructions for Natural Language Generation (NLG) tasks. Despite the impressive capabilities of Large Language Models (LLMs) in understanding and executing a wide range of tasks through natural language interaction, their performance is significantly hindered by the ambiguity present in real-world instructions. To address this, AmbigNLG seeks to identify and mitigate such ambiguities, aiming to refine instructions to match user expectations better. We introduce a dataset, AmbigSNI-NLG, consisting of 2,500 instances, and develop an ambiguity taxonomy for categorizing and annotating instruction ambiguities. Our approach demonstrates substantial improvements in text generation quality, highlighting the critical role of clear and specific instructions in enhancing LLM performance in NLG tasks.

翻译：在本研究中，我们提出AmbigNLG这一新任务，旨在解决自然语言生成（NLG）任务指令中的任务歧义挑战。尽管大语言模型（LLMs）通过自然语言交互理解和执行各类任务的能力令人瞩目，但其性能仍受到现实世界指令中歧义性的显著制约。为此，AmbigNLG致力于识别并缓解此类歧义，以期优化指令使其更贴合用户预期。我们构建了包含2500个实例的数据集AmbigSNI-NLG，并开发了一套用于分类和标注指令歧义的歧义分类体系。实验表明，本方法显著提升了文本生成质量，凸显了清晰明确的指令在增强LLMs NLG任务性能中的关键作用。