Summary of A Review on Scientific Knowledge Extraction Using Large Language Models in Biomedical Sciences, by Gabriel Lino Garcia et al.
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
by Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, Lucas Miranda, Maria Paola de Salvo, João Paulo Papa
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews the current state-of-the-art applications of large language models (LLMs) in biomedical research, particularly in evidence synthesis and data extraction. LLMs have shown remarkable potential, but significant challenges remain, including hallucinations, contextual understanding, and generalizability across diverse medical tasks. The study highlights gaps in the current literature, emphasizing the need for unified benchmarks to ensure reliability in real-world applications. To address these challenges, the paper proposes integrating state-of-the-art techniques like retrieval-augmented generation (RAG) to enhance LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help with finding and organizing medical information. These models are really good at learning from lots of text, but they have some problems too, like not understanding what’s going on in a specific situation or making things up that aren’t true. The study talks about the challenges these models face and suggests ways to make them better, like combining different approaches together. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
