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Meta-Task Prompting Elicits Embeddings from Large Language Models

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arxiv 2402.18458 v2 pith:BLA5JPEP submitted 2024-02-28 cs.CL

Yibin Lei , Di Wu , Tianyi Zhou , Tao Shen , Yu Cao , Chongyang Tao , Andrew Yates This is my paper · ORCID
classification cs.CL
keywords embeddingsmeta-taskmodelspromptingembeddinglanguagelargellms
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FreeRet: MLLMs as Training-Free Retrievers

    cs.CV 2025-09 unverdicted novelty 6.0 of 10

    FreeRet enables pretrained MLLMs to act as training-free retrievers via semantically grounded embeddings and reasoning-based reranking, outperforming models trained on millions of pairs on MMEB benchmarks.

  2. DeepRTL2: A Versatile Model for RTL-Related Tasks

    cs.AR 2025-05 reject novelty 6.0 of 10

    DeepRTL2 claims state-of-the-art results across RTL generation, understanding, code search, equivalence checking, and performance prediction, but the evidence is weakened by benchmark construction issues and a contrad...

  3. Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Citss combines sentence-level cropping and keyphrase perturbation with contrastive learning to fine-tune both encoder and decoder language models for citation classification.

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