Meta-Task Prompting Elicits Embeddings from Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911962953154560 |
|---|---|
| author | Lei, Yibin Wu, Di Zhou, Tianyi Shen, Tao Cao, Yu Tao, Chongyang Yates, Andrew |
| author_facet | Lei, Yibin Wu, Di Zhou, Tianyi Shen, Tao Cao, Yu Tao, Chongyang Yates, Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta-Task Prompting Elicits Embeddings from Large Language Models Lei, Yibin Wu, Di Zhou, Tianyi Shen, Tao Cao, Yu Tao, Chongyang Yates, Andrew Computation and Language 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. |
| title | Meta-Task Prompting Elicits Embeddings from Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.18458 |