Meta-Task Prompting Elicits Embeddings from Large Language Models
- URL: http://arxiv.org/abs/2402.18458v2
- Date: Mon, 22 Jul 2024 09:35:08 GMT
- Title: Meta-Task Prompting Elicits Embeddings from Large Language Models
- Authors: Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates,
- Abstract summary: We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation.
We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning.
Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
- Score: 54.757445048329735
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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