[論文レビュー] Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology

Tiago Lubiana, Rafael de Figueiredo Lopes+5 more|arXiv (Cornell University)|Mar 29, 2023

Artificial Intelligence in Healthcare and Education被引用数 13

ひとこと要約

この論文は、計算生物学者がChatGPT/GPT-4を用いてワークフローを最適化するための実用的なヒントとプロンプトを十個提供し、コードリファクタリング、科学的執筆、プロンプトエンジニアリングを網羅しており、進行中のアイデアのためのGitHubリポジトリがリンクされている。

ABSTRACT

The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist computational biologists in optimizing their workflows. We have collected relevant prompts and reviewed the nascent literature in the field, compiling tips we project to remain pertinent for future ChatGPT and LLM iterations, ranging from code refactoring to scientific writing to prompt engineering. We hope our work will help bioinformaticians to complement their workflows while staying aware of the various implications of using this technology. Additionally, to track new and creative applications for bioinformatics tools such as ChatGPT, we have established a GitHub repository at https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical adherence to ChatGPT and other LLMs will increase the efficiency of computational biologists, ultimately advancing the pace of scientific discovery in the life sciences.

研究の動機と目的

• Motivate the use of ChatGPT/GPT-4 in computational biology and outline practical usage scenarios.
• Provide a curated set of actionable tips to optimize workflows and communication with LLMs.
• Summarize prompts and strategies to improve code, analysis, and scientific writing in bioinformatics.
• Highlight ethical considerations and future-proofing as LLM iterations evolve.

提案手法

• Review of existing literature on ChatGPT/GPT-4 applicability in computational biology.
• Compilation of ten practical tips based on authors’ experience and nascent literature.
• Collection and review of relevant prompts for coding, analysis, and writing tasks.
• Reference to an accompanying GitHub repository for tracking new applications and prompts.

実験結果

リサーチクエスチョン

• RQ1 What practical tips best help computational biologists harness ChatGPT/GPT-4 effectively?
• RQ2 How can prompts be engineered to improve coding, data analysis, and scientific writing in bioinformatics?
• RQ3 What ethical and future-proofing considerations should researchers keep in mind when using LLMs in computational biology?

主な発見

• Ten actionable tips cover areas from code refactoring to scientific writing and prompt engineering.
• Prompts and workflows are collected and reviewed to remain applicable across future LLM iterations.
• An accompanying GitHub repository is established to track new and creative bioinformatics applications of ChatGPT/GPT-4.