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Google Embeddings 2 leads retrieval benchmarks but lags in speed

By PulseAugur Editorial · [2 sources] ·

A new paper benchmarks Google Embeddings 2 (GE2) against several open-source models for multilingual dense retrieval and RAG systems. GE2 achieved top performance across multiple tasks, including BEIR and an Italian RAG corpus, but exhibited significantly higher latency compared to local models. Multilingual-E5-large (mE5-L) offered comparable performance on Italian retrieval with much lower latency, making it a more practical choice for applications with strict response time requirements. AI

IMPACT Highlights trade-offs between cutting-edge performance and latency in retrieval models, guiding practical deployment choices.

RANK_REASON The cluster contains an academic paper evaluating AI models on specific benchmarks.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Giandomenico Solimando ·

    Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems

    arXiv:2605.23618v1 Announce Type: new Abstract: We benchmark Google Embeddings (GE2), a Vertex-AI-hosted bi-encoder with 2,048-token context and explicit task-type conditioning, against five open-source alternatives: BGE-M3, E5-large, Multilingual-E5-large (mE5-L), LaBSE, and Par…

  2. arXiv cs.CL TIER_1 English(EN) · Giandomenico Solimando ·

    Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems

    We benchmark Google Embeddings (GE2), a Vertex-AI-hosted bi-encoder with 2,048-token context and explicit task-type conditioning, against five open-source alternatives: BGE-M3, E5-large, Multilingual-E5-large (mE5-L), LaBSE, and Paraphrase-Multilingual-MPNet (mMPNet). Evaluation …

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