LLM2Vec: A Simple AI Approach to Transform Any Decoder-Only LLM into a Text Encoder Achieving SOTA Performance on MTEB in the Unsupervised and Supervised Category

Natural Language Processing (NLP) tasks heavily rely on text embedding models as they translate the semantic meaning of text into vector representations. These representations make it possible to quickly complete a variety of NLP tasks, including information retrieval, grouping, and semantic textual similarity.  Pre-trained bidirectional encoders or encoder-decoders, such as BERT and T5, have historically […]

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ai paper summary ai shorts applications artificial intelligence decoder editors pick embedding embedding models encoder language language model language processing large language model llm meaning natural natural language natural language processing nlp performance processing semantic simple sota staff tasks tech news technology text text embedding translate unsupervised vector