  We introduce a new unsupervised text embedding method, Meta-Task Prompting
with Explicit One-Word Limitation (MetaEOL), for generating high-quality
sentence embeddings from Large Language Models (LLMs) without the need for
model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to
produce embeddings through a series of carefully designed prompts that address
multiple representational aspects. Our comprehensive experiments demonstrate
that embeddings averaged from various meta-tasks are versatile embeddings that
yield competitive performance on Semantic Textual Similarity (STS) benchmarks
and excel in downstream tasks, surpassing contrastive-trained models. Our
findings suggest a new scaling law, offering a versatile and resource-efficient
approach for embedding generation across diverse scenarios.
