PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficiency and cross-architecture transferability. Whenever a new backbone emerges, existing approaches require costly retraining from scratch. To address this, we propose PromptEmbedder, a novel dual-LLM framework that decouples embedding knowledge from specific backbone weights. PromptEmbedder utilizes a Prompting LLM to generate instruction-aware soft prompts for a frozen Embedding LLM via a differentiable generation process with continuous relaxation, ensuring full gradient flow during contrastive training. By loca...
| Authors | Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen et al. |
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| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 9 June 2026 |
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