Papers by Ruyi Ji

2 papers
Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)

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Challenge: Recent research focuses on improving prediction performance and reliability of LLM.
Approach: They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM.
Outcome: The proposed method improves performance on knowledge-based VQA benchmarks.
Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale (2024.emnlp-main)

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Challenge: Multimodal large language models (MLLMs) lack visual knowledge in medical applications due to data privacy concerns and high annotation costs.
Approach: They refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) to denoise and reformat the data.
Outcome: The proposed model significantly improves the MMMU Health & Medicine track and shows that it can be used in multimodal scenarios.

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