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Computer Science > Computation and Language

arXiv:2401.00368 (cs)
[Submitted on 31 Dec 2023]

Title:Improving Text Embeddings with Large Language Models

Authors:Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan
Majumder, Furu Wei
Download a PDF of the paper titled Improving Text Embeddings with
Large Language Models, by Liang Wang and 5 other authors
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    Abstract:In this paper, we introduce a novel and simple method
    for obtaining high-quality text embeddings using only synthetic
    data and less than 1k training steps. Unlike existing methods
    that often depend on multi-stage intermediate pre-training with
    billions of weakly-supervised text pairs, followed by fine-tuning
    with a few labeled datasets, our method does not require building
    complex training pipelines or relying on manually collected
    datasets that are often constrained by task diversity and
    language coverage. We leverage proprietary LLMs to generate
    diverse synthetic data for hundreds of thousands of text
    embedding tasks across nearly 100 languages. We then fine-tune
    open-source decoder-only LLMs on the synthetic data using
    standard contrastive loss. Experiments demonstrate that our
    method achieves strong performance on highly competitive text
    embedding benchmarks without using any labeled data. Furthermore,
    when fine-tuned with a mixture of synthetic and labeled data, our
    model sets new state-of-the-art results on the BEIR and MTEB
    benchmarks.

Comments: 15 pages, 8 tables
Subjects: Computation and Language (cs.CL); Information Retrieval
          (cs.IR)
Cite as:  arXiv:2401.00368 [cs.CL]
          (or arXiv:2401.00368v1 [cs.CL] for this version)

Submission history

From: Liang Wang [view email]
[v1] Sun, 31 Dec 2023 02:13:18 UTC (129 KB)
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