Med-GRIM, combining the BIND encoder with graph retrieval and small language models, reports 83.33% accuracy on a new 30-question DermaGraph dermatology benchmark, outperforming several larger medical VLMs in a zero-shot setting.
Can we Trust Chatbots for now? Accuracy, reproducibility, traceability; a Case Study on Leonardo da Vinci's Contribution to Astronomy
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abstract
Large Language Models (LLM) are studied. Applications to chatbots and education are considered. A case study on Leonardo's contribution to astronomy is presented. Major problems with accuracy, reproducibility and traceability of answers are reported for ChatGPT, GPT-4, BLOOM and Google Bard. Possible reasons for problems are discussed and some solutions are proposed.
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Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG
Med-GRIM, combining the BIND encoder with graph retrieval and small language models, reports 83.33% accuracy on a new 30-question DermaGraph dermatology benchmark, outperforming several larger medical VLMs in a zero-shot setting.