LLM Embeddings

LLM embeddings are vector representations of text generated by large language models (LLMs), aiming to capture semantic meaning for various downstream tasks. Current research focuses on improving embedding quality for tasks like model selection, anomaly detection, and financial analysis, often employing contrastive learning and graph neural networks to enhance performance and efficiency. These advancements are significant because they enable more effective utilization of LLMs across diverse applications, from improving search and recommendation systems to facilitating more robust and nuanced financial modeling.