Summary of A Simplified Retriever to Improve Accuracy Of Phenotype Normalizations by Large Language Models, By Daniel B. Hier and Thanh Son Do and Tayo Obafemi-ajayi
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
by Daniel B. Hier, Thanh Son Do, Tayo Obafemi-Ajayi
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 introduces a novel approach to enhance the accuracy of large language models (LLMs) in phenotype term normalization tasks. The authors propose a simplified retriever that leverages contextual word embeddings from BioBERT to search for candidate matches in the Human Phenotype Ontology (HPO). This method improves LLM accuracy by 28 percentage points compared to the baseline, achieving an accuracy of 90.3% when tested on OMIM-derived terms. The approach shows promise for generalization to other biomedical term normalization tasks and offers a more efficient alternative to complex retrieval methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand medical terms by using a new way to search for related words in a big database called the Human Phenotype Ontology (HPO). The authors tested this method on medical descriptions from OMIM and found that it made a language model more accurate, increasing its score by 28 percentage points. This approach could be used to improve computers’ understanding of many different types of biomedical terms. |
Keywords
» Artificial intelligence » Generalization » Language model
