引入了一个名为VIBE的新基准来评估近似最近邻（ANN）搜索算法，通过使用代表检索增强生成（RAG）等现代应用的代表性数据集来解决现有基准的局限性。VIBE框架包括一个用于生成具有密集嵌入模型和非分布外数据集的基准数据集的管道，以模拟真实世界的负载。此外，研究表明，微调嵌入模型对于领域特定相关性具有成本效益，并且有效的分块策略对于检索质量至关重要，其重要性往往超过嵌入模型本身。与专有模型相比，开放嵌入模型在检索质量方面具有竞争力，尤其是在自定义数据集上进行评估时，而不是仅仅依赖于基准测试。
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arXiv:2505.17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performance of vector indexes for ANN search. However, th…
Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction. Table-level embeddings in particular underpin a wide range of applications, including table retrieval, data lake discovery, and table classification. De…
arXiv:2608.06972v1 Announce Type: new Abstract: Embeddings have emerged as a standard representational interface linking foundation models with downstream systems. Most embedding benchmarks assess representations through discriminative tasks or geometric criteria centered on sepa…
<!-- SC_OFF --><div class="md"><p>What Local Embedding + Reranking Models are you guys running for RAG? </p> <p>I went down this rabbit hole because I wanted a Embedding Model + Reranker for a Translation Memory Server. </p> <p>Essentially, given X phrase/word/sentence/paragraph …
<p>Fine-tuning an embedding model is unusually cheap — a base model of a hundred million parameters, fifty thousand pairs and an hour on one GPU. What is not cheap is everything that happens after: a fine-tuned model is a new model, and every vector you have stored becomes incomp…
<p>RAG (Retrieval-Augmented Generation - a pattern where you fetch relevant documents before generating an answer) lives or dies on retrieval quality. A cheaper retrieval stack isn't always a compromise.</p> <h2> The Retrieval Quality Gap Is Often in the Chunking, Not the Model <…
