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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
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 4 Pith papers

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

  1. mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    mEOL creates aligned embeddings for text, images, and SVGs using instruction-guided MLLM one-word summaries and semantic SVG rewriting, outperforming baselines on a new text-to-SVG retrieval benchmark.

  2. 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.

  3. FreeRet: MLLMs as Training-Free Retrievers

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A prompt-and-layer tweak lets pretrained multimodal LLMs serve as competitive retrieval systems without any additional training, with reranking framed as multiple-choice questions to reduce label bias.

  4. DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    DocRetriever introduces a framework using layout-aware sparse embeddings for hybrid encoding without OCR and a generalizable reasoning-augmented reranker for few-shot settings, plus the MultiDocR benchmark for evaluation.

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