CESAR：面向多轮对话的组合指令自动归纳
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
AI总结：
本文提出CESAR框架，通过统一格式并自动归纳组合指令，构建了包含63个数据集、86个基础任务和68个复合任务的InstructDial++基准，使模型能遵循多约束组合提示。
摘要：
基于指令的多任务处理在大型语言模型（LLMs）于多轮对话应用中取得成功的过程中发挥了关键作用。尽管公开可用的大型语言模型已展现出良好的性能，但当面对带有多个约束的复杂指令时，它们仍落后于ChatGPT等最先进的模型。在本工作中，我们假设大规模复杂示范的可用性对于弥合这一差距至关重要。聚焦于对话应用，我们提出了一个新颖的框架CESAR，该框架以相同格式统一大量对话任务，并允许在无需任何人工努力的情况下程序化地归纳复杂指令。我们将CESAR应用于InstructDial，这是一个用于基于指令的对话任务的基准。我们进一步用新的数据集和任务增强InstructDial，并利用CESAR归纳具有组合指令的复杂任务。由此产生了一个名为InstructDial++的新基准，它包含63个数据集、86个基础任务和68个复合任务。通过严格的实验，我们证明了CESAR在提供丰富指令方面的可扩展性。在InstructDial++上训练的模型能够遵循组合提示，例如要求多个风格约束的提示。
英文摘要：
Instruction-based multitasking has played a critical role in the success of large language models (LLMs) in multi-turn dialog applications. While publicly available LLMs have shown promising performance, when exposed to complex instructions with multiple constraints, they lag against state-of-the-art models like ChatGPT. In this work, we hypothesize that the availability of large-scale complex demonstrations is crucial in bridging this gap. Focusing on dialog applications, we propose a novel framework, CESAR, that unifies a large number of dialog tasks in the same format and allows programmatic induction of complex instructions without any manual effort. We apply CESAR on InstructDial, a benchmark for instruction-based dialog tasks. We further enhance InstructDial with new datasets and tasks and utilize CESAR to induce complex tasks with compositional instructions. This results in a new benchmark called InstructDial++, which includes 63 datasets with 86 basic tasks and 68 composite tasks. Through rigorous experiments, we demonstrate the scalability of CESAR in providing rich instructions. Models trained on InstructDial++ can follow compositional prompts, such as prompts that ask for multiple stylistic constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
