This research comprehensively evaluates large language models (LLMs) for RNA secondary structure prediction, revealing their current capabilities and limitations.

• Examines how LLMs can learn rich RNA representations from sequence data to enhance structure prediction
• Benchmarks multiple RNA-specific language models against traditional methods
• Identifies key strengths and areas for improvement in applying LLMs to RNA biology
• Provides insights for developing more accurate computational approaches for RNA structure analysis

Accurate RNA structure prediction is fundamental to understanding cellular function, disease mechanisms, and potential therapeutic targets in molecular biology.

Comprehensive benchmarking of large language models for RNA secondary structure prediction