Text clustering with LLM embeddingsarxiv.org/abs/2403.15112 核心观点: 这篇文章探讨了文本聚类中使用不同文本嵌入(特别是大语言模型中的嵌入)和聚类算法对聚类结果的影响。文章进行了多组实验,评估了嵌入方式、降维和嵌入维度对聚类结果的影响。结果显示,大语言模型中的嵌入擅长捕捉结构化语言的细微差别,BERT在...
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对于《Improving Text Embeddings with Large Language Models》一文总结就是以下几点: 构造高质量训练数据 文本向量表征时写好提示词 选对底座大模型 数据构造 数据构造方法一般根据已有文档生成查询Query、伪标签或者根据查询Query生成伪文档等,而本文直接挖掘大模型内部存储的知识内容,在不依赖已有文档或查询Query的情况下...
Text clustering is a cornerstone task in natural language processing with a broad spectrum of applications. Given the advancements in large language models, employing such models to enhance general text clustering has shown promising potential in boosting clustering effectiveness. However, current LLMs-dr...
OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are commonly used for: Search (where results are ranked by relevance to a query string) Clustering (where text strings are grouped by similarity) Recommendations (where items with related text strings are recommended) Anoma...
1. Original embeddings cdperspective/2_finetune bash scripts/get_embedding.sh The embeddings are produced in each folder ofdatasets. It will also save the clustering measures. Details instructions see bash script. E5 embeddings are produced withscripts/get_embedding_e5.sh. ...
MTEB: This benchmark covers 56 different tasks, including retrieval, classification, re-ranking, clustering, summarization, and more. Depending on your goals, you can look at the precise subset of tasks representing your use case. BEIR: This benchmark focuses on the retrieval task and adds compl...
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代码:embeddings-benchmark/mteb :大规模文本嵌入评估 中文文本嵌入评估:CMTEB 向量的检索 向量搜索库 Approximate Nearest Neighbor(ANN)是一种用于在大规模数据集中寻找最近邻居的算法。其目标是在尽可能短的时间内找到与给定查询点最近的数据点,但不一定是确切的最近邻。为了达到这个目标,ANN使用了一些启发式方法,例...
Hierarchical clustering with agglomerative clustering The resulting sentence embeddings from the cambridgeltl/SapBERT-from-PubMedBERT-fulltext24,35model are put through a hierarchical agglomerative clustering algorithm29,30that works in a bottom-up approach. This process begins by treating each sentence as...