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Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples

23 Feb 2024arXiv:2402.15132archive 2025-07-28

Soma Sato, Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda

Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a decoder-based model, PromptEOL, has achieved the best performance on semantic textual similarity (STS) tasks. However, PromptEOL requires a manually annotated natural language inference (NLI) dataset for fine-tuning. We aim to improve sentence embeddings without using large manually annotated datasets by automatically generating an NLI dataset with an LLM and using it for fine-tuning of PromptEOL. To achieve this, we explore methods of data generation suitable for sentence embedding learning in this study. Specifically, we will focus on automatic dataset generation through few-shot learning and explore the appropriate methods to leverage few-shot examples. Experimental results on the STS tasks demonstrate that our approach outperforms existing models in settings without large manually annotated datasets.

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build_fewshot_examples lamsoma/auto_nli/n-shot.py official repository ran · our draft was wrong no licence file found · pointer only · 51409ba20eda4eb2 · report
create_contradiction_example lamsoma/auto_nli/n-shot.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · d8f5282a4befa488 · report
create_entailment_example lamsoma/auto_nli/n-shot.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · bd13a26a38b66589 · report
generate_sentemb_prompt lamsoma/auto_nli/ft_llm.py official repository ran · our draft was wrong no licence file found · pointer only · 35f88896173b1908 · report

Tasks

Dataset GenerationDecoderFew-Shot LearningNatural Language InferenceSTSSemantic Textual SimilaritySentenceSentence EmbeddingSentence EmbeddingsSentence-Embedding

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