Embracing Foundation Models for Advancing Scientific Discovery.

Area of Science:

• Artificial Intelligence
• Scientific Discovery
• Computational Science

Background:

• Machine learning foundation models, including large language models (LLMs), have transformed computer vision and natural language processing.
• Recent research explores using these models for hypothesis generation to assist human researchers.

Purpose of the Study:

• To envision and outline a future where foundation models are integrated into academia to accelerate scientific discovery.
• To address the challenges of harnessing LLM knowledge for research and evaluating their effectiveness.

Main Methods:

• Proposing knowledge-grounded Chain-of-Idea (KG-CoI) for hypothesis generation.
• Introducing IdeaBench, a customizable framework for benchmarking LLM hypothesis generators.

Main Results:

• The paper outlines a vision for integrating foundation models into the scientific discovery process.
• It addresses key challenges in leveraging LLM parametric knowledge and evaluation methods.

Conclusions:

• Foundation models offer significant potential to enhance and accelerate scientific discovery.
• This work lays the groundwork for future human-AI collaboration in research through novel methods and evaluation tools.