LlamaIndex 分享了提升静态嵌入模型检索性能的实验，虽然结果不理想，但提供了宝贵经验。
LlamaIndex 研究了静态嵌入模型在检索任务中的表现。静态嵌入模型提供无与伦比的吞吐量，但相比传统嵌入模型存在准确度损失。研究团队尝试了多种改进方法，包括在每词嵌入上进行原始最大相似度评分、训练小型适配器模型以及改变蒸馏训练目标和教师模型。尽管这些方法未能达到预期效果，但研究深入探讨了静态嵌入模型的工作原理和局限性。
Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to tr...
Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make them better for retrieval? We tried several things: ✅️ raw maxsim scoring on the per-token embeddings ✅️ training a small adapter model ✅️ changing the distillation training target and teacher While none of these saw the results we wanted, it's an excellent dive into static embeddings and how they do (and don't) work! Blog: llamaindex.ai/blog/exploring… 💬 1 🔄 2 ❤️ 4 👀 480 📊 2 ⚡