New RAG Benchmark Launched for Drug Discovery PPIs

Researchers have introduced RAGPPI, a new benchmark designed to evaluate Retrieval-Augmented Generation (RAG) systems for identifying the biological impacts of protein-protein interactions (PPIs) in drug discovery. The benchmark consists of 4,420 question-answer pairs, with a gold-standard subset of 500 pairs created through expert annotation and a silver-standard set generated using an ensemble auto-evaluation LLM. RAGPPI aims to advance RAG systems for drug discovery applications by providing a dedicated resource for this specific task. AI

RANK_REASON The cluster describes a new academic paper introducing a benchmark dataset for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• drug discovery
• Large Language Models
• protein-protein interactions
• RAGPPI
• Retrieval-Augmented Generation
• Youngseung Jeon

• paper
• product

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