[論文レビュー] Techniques for supercharging academic writing with generative AI

Zhicheng Lin|arXiv (Cornell University)|Oct 26, 2023

Artificial Intelligence in Healthcare and Education参考文献 4被引用数 10

ひとこと要約

大規模言語モデルを用いた人間とAIの協働フレームワークを提示し、学術文章の質を高めるためのプロンプト戦略、プロセス段階、厳格な学術研究を守る安全策を示す視点。

ABSTRACT

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.

研究の動機と目的

• AIがなぜ、どのように、何を支援すべきかを説明する学術執筆における人間–AI協働フレームワークを定義する。
• AIの関与を指針とする人間–AI協働執筆の二段階モデル（計画と下書き/編集）を説明する。
• 学術標準およびジャーナルポリシーと整合するAI執筆支援の種類とレベルを特徴づける。
• 執筆ワークフローにAIを効果的に組み込むためのアウトライン作成、下書き、編集の実践的なプロンプト技法を提供する。
• 過度依存のリスクに対処し、厳密で多様性のある高品質な科学的コミュニケーションを維持する戦略を提案する。

提案手法

• 執筆におけるAI関与の理由付け（なぜ）、プロセス（どう）、性質（何）を分離する人間–AI協働フレームワークを提案する。
• 相互作用を構築するための人間–AI協働執筆の二段階モデル（計画、下書き/編集）を導入する。
• AIの寄与の種類とレベルの観点から執筆支援をモデル化し、責任ある利用を導く。
• アウトライン作成、下書き、編集のための効果的なプロンプト技法を説明し、AIを標準的な執筆ルーティンに組み込む。
• 学術的厳密性を維持し、ジャーナルポリシーに準拠し、AIへの過度の依存を緩和する戦略を概説する。

実験結果

リサーチクエスチョン

• RQ1 What principles and methods best enable productive, responsible collaboration between humans and generative AI in academic writing?
• RQ2 How can a two-stage human–AI workflow (planning and drafting/editing) be operationalized in scholarly writing?
• RQ3 What are the appropriate types and levels of AI assistance that preserve rigor and integrity across journals and disciplines?
• RQ4 Which prompting techniques optimally support outlining, drafting, and editing when using LLMs in academic writing?
• RQ5 What safeguards and practices reduce overreliance and promote diversity and quality in scientific communication?

主な発見

• A human–AI collaborative framework clarifies rationale, process, and nature of AI support in writing.
• A two-stage model (planning vs. drafting/editing) structures effective and safe AI engagement.
• Prompting techniques can be tailored to outlining, drafting, and editing to integrate AI smoothly into the workflow.
• Strategies are proposed to maintain rigorous scholarship and comply with varied journal policies.
• Using AI in writing can ease communication burden, empower authors, and promote diversity in science when applied prudently.