Text embedding models are designed for sentence-level applications like retrieval and semantic similarity, and are primarily evaluated on sentence-level benchmarks. Their behavior on isolated words is less understood. We show that simply prepending semantic prompts to words before embedding substantially improves word similarity correlations. Testing 7 text embedding models, including text-embedding-3-large (OpenAI), embed-english-v3.0 (Cohere), voyage-3(Voyage AI), all-mpnet-base-v2, and Qwen3-Embedding-8B, on 3 standard benchmarks (SimLex-999, WordSim-353, MEN-3000), we find that prompts like "meaning: {word}" or "Represent the semantic concept: {word}" improve Spearman correlations by up to +0.29 on SimLex-999. Some models fail completely on bare words (correlation = 0) but recover with prompts (+0.73 improvement). Our best results achieve correlation = 0.692 on SimLex-999 with embed-english-v3.0 (Cohere), correlation = 0.811 on WordSim-353, and correlation = 0.855 on MEN-3000 with text-embedding-3-large (OpenAI). These results outperform classic static embeddings like Word2Vec (correlation = 0.40) and even the best static method LexVec (correlation = 0.48) on SimLex-999, establishing a new state-of-the-art for pure embedding methods. This zero-shot technique requires no training and works with any text embedding model.

翻译：文本嵌入模型通常为句子级应用（如检索和语义相似度）设计，并主要在句子级基准测试中评估。它们对孤立词汇的处理机制尚不明确。本文研究表明，在嵌入前为词汇添加语义提示能显著提升词相似度相关性。我们测试了7种文本嵌入模型，包括text-embedding-3-large（OpenAI）、embed-english-v3.0（Cohere）、voyage-3（Voyage AI）、all-mpnet-base-v2和Qwen3-Embedding-8B，在3个标准基准（SimLex-999、WordSim-353、MEN-3000）上发现，使用“含义：{词}”或“表征语义概念：{词}”等提示可将SimLex-999的斯皮尔曼相关性最高提升+0.29。部分模型对原始词汇完全失效（相关性=0），但通过提示可恢复性能（最高提升+0.73）。我们的最佳结果包括：embed-english-v3.0（Cohere）在SimLex-999上达到相关性=0.692，text-embedding-3-large（OpenAI）在WordSim-353上达到相关性=0.811，在MEN-3000上达到相关性=0.855。这些结果超越了经典静态嵌入方法如Word2Vec（相关性=0.40）及最优静态方法LexVec（相关性=0.48），在SimLex-999上为纯嵌入方法确立了新的技术标杆。该零样本技术无需训练，适用于任何文本嵌入模型。