论文标题
一个嵌入者，任何任务：指令 - 字段嵌入
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
论文作者
论文摘要
我们介绍了指导员，这是一种用于计算给定任务说明的文本嵌入的新方法：每个文本输入都嵌入到解释用例（例如，任务和域描述）的指令。与先前工作更专业的编码器不同，教师是一个单个嵌入器，可以生成针对不同下游任务和域的文本嵌入，而无需任何进一步的培训。我们首先注释了330个不同任务的说明，并在此多任务混合物上培训讲师，并造成对比损失。我们在70个嵌入评估任务（在培训期间看不见的66个）评估讲师，从分类和信息检索到语义文本相似性和文本生成评估。讲师的参数比以前的最佳模型少了，但与以前的70种不同数据集的最佳结果相比，平均提高了3.4％。我们的分析表明，讲师对说明的变化是强大的，并且指令填充减轻了培训单个模型在不同数据集上的挑战。我们的模型，代码和数据可从https://instructor-embedding.github.io获得。
We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io.
