  Large decoder-only language models (LLMs) are the state-of-the-art models on
most of today's NLP tasks and benchmarks. Yet, the community is only slowly
adopting these models for text embedding tasks, which require rich
contextualized representations. In this work, we introduce LLM2Vec, a simple
unsupervised approach that can transform any decoder-only LLM into a strong
text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional
attention, 2) masked next token prediction, and 3) unsupervised contrastive
learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3
popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed
models on English word- and sequence-level tasks. We outperform encoder-only
models by a large margin on word-level tasks and reach a new unsupervised
state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
Moreover, when combining LLM2Vec with supervised contrastive learning, we
achieve state-of-the-art performance on MTEB among models that train only on
publicly available data. Our strong empirical results and extensive analysis
demonstrate that LLMs can be effectively transformed into universal text
encoders in a parameter-efficient manner without the need for expensive
adaptation or synthetic GPT-4 generated data.
