Brief

Researchers have developed an automated system to classify psychiatric diagnoses using Natural Language Processing (NLP) and Machine Learning (ML). The study evaluated various text representation methods, including classical models and Large Language Models (LLMs) like e5_large, BioLORD, and Llama-3-8B, on a dataset of over 145,000 Spanish psychiatric descriptions. The findings indicate that transformer-based embeddings significantly outperform traditional methods, with the fine-tuned e5_large model achieving a top F1 score of 0.866. This work highlights the importance of adapting LLMs to specialized clinical language for accurate diagnosis coding. AI

A new paper benchmarks Google Embeddings 2 (GE2) against several open-source models for multilingual dense retrieval and RAG systems. GE2 achieved top performance across multiple tasks, including BEIR and an Italian RAG corpus, but exhibited significantly higher latency compared to local models. Multilingual-E5-large (mE5-L) offered comparable performance on Italian retrieval with much lower latency, making it a more practical choice for applications with strict response time requirements. AI