Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.
翻译:教学调整是国家语言平台的一个新兴范例,其中自然语言指令与语言模型一起被利用,以促成在未见任务上零发性能; 已经展示了相关指令,以便能够在大型和小型语言模型的无形任务和数据集上取得良好业绩; 对话是一个特别有趣的领域,以探索教学调整,因为对话系统执行与语言有关的多种任务(例如,自然语言理解和生成、特定领域的互动),然而,对与对话有关的任务没有进行系统探索; 我们引入了教学调整框架 " 指令 ",即对话指导调整框架,由来自59个公开的对话框数据集创建的统一文本对文本格式的48个不同对话任务储存库组成; 下一步,我们探索对不同对话任务中指令调整的模型的交叉任务概括性能力。 我们的分析表明,指令Dial使得在对话评估和意向检测等未知数据集和任务上能够很好地进行零发性性性工作,甚至可以在几眼环境中进行更好的业绩。 为确保模型遵循指令,我们引入新的元任务。 我们用拟议框架为多式对话任务设定基准零发和几发式。