AmbigNLG: Addressing Task Ambiguity in Instruction for NLG
Ayana Niwa Hayate Iso
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.