Summary of Generalist Embedding Models Are Better at Short-context Clinical Semantic Search Than Specialized Embedding Models, by Jean-baptiste Excoffier et al.
Generalist embedding models are better at short-context clinical semantic search than specialized embedding models
by Jean-Baptiste Excoffier, Tom Roehr, Alexei Figueroa, Jens-Michalis Papaioannou, Keno Bressem, Matthieu Ortala
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study investigates the robustness and reliability of Large Language Models (LLMs) in the medical domain. Researchers constructed a textual dataset based on ICD-10-CM code descriptions, widely used in US hospitals, and tested various embedding models’ performance in a semantic search task. The goal was to correctly match rephrased texts to original descriptions. Results showed that generalist models outperformed clinical models, suggesting that existing clinical specialized models are more sensitive to small input changes that confuse them. This highlights the problem of insufficient training data and lack of global language understanding for accurate medical document handling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are used in medicine to help doctors and researchers find important information quickly. But sometimes, these models get confused if the words are changed a little bit. Researchers wanted to see how well these models work when they’re asked to match similar texts together. They made a special dataset using medical code descriptions from hospitals and tested different kinds of models. The results showed that general-purpose models were better at matching texts than specialized medical models. This means that doctors might need to use more robust models in the future. |
Keywords
» Artificial intelligence » Embedding » Language understanding
