New BioMol-MQA dataset challenges LLMs with multi-modal bio-molecular reasoning

Researchers have introduced BioMol-MQA, a novel dataset designed to enhance Large Language Model (LLM) reasoning capabilities over multi-modal bio-molecular interactions. The dataset includes a multimodal knowledge graph incorporating text and molecular structures, alongside challenging questions that require LLMs to retrieve and synthesize information from these diverse sources. Current LLMs demonstrate significant limitations in answering these questions, highlighting the need for advanced retrieval-augmented generation (RAG) frameworks tailored for complex, multi-modal data. AI

IMPACT This dataset could drive advancements in LLM capabilities for complex scientific domains requiring multi-modal data integration and reasoning.

RANK_REASON The cluster describes a new academic dataset and paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• BioMol-MQA
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• LLM
• retrieval-augmented generation
• Saptarshi Sengupta
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →