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：本研究提出了AmbigNLG，这是一个旨在应对自然语言生成任务指令中任务模糊性挑战的新任务。尽管大型语言模型在通过自然语言交互理解和执行广泛任务方面展现出令人印象深刻的能力，但其性能受到现实世界指令中存在的模糊性的显著制约。为解决此问题，AmbigNLG致力于识别并缓解此类模糊性，旨在优化指令以更好地匹配用户预期。我们构建了一个包含2500个实例的数据集AmbigSNI-NLG，并开发了用于分类和标注指令模糊性的模糊性分类体系。我们的方法在文本生成质量上展现出显著提升，突显了清晰明确的指令对于增强LLM在NLG任务中性能的关键作用。