PulseAugur
实时 23:36:49
实体 MTEB

MTEB

PulseAugur coverage of MTEB — every cluster mentioning MTEB across labs, papers, and developer communities, ranked by signal.

Show in brief
总计 · 30天
7
90 天内 7
发布 · 30天
0
90 天内 0
论文 · 30天
5
90 天内 5
层级分布 · 90 天
情绪 · 30 天

2 天有情绪数据

最近 · 第 1/1 页 · 共 7 条
  1. RESEARCH · CL_43997 ·

    Embedding models' structure predicts benchmark performance, study finds

    Researchers have demonstrated that the organization of embedding spaces within high-performing models consistently predicts their benchmark performance. By evaluating 25 embedding models across five MTEB tasks, they fou…

  2. TOOL · CL_39077 ·

    Hugging Face releases Ettin Reranker models for improved search

    Hugging Face has released a new family of six Ettin Reranker models, built on top of Ettin ModernBERT encoders. These models offer state-of-the-art performance for their respective sizes and are designed for the retriev…

  3. TOOL · CL_22216 ·

    LMEB benchmark evaluates long-horizon memory retrieval beyond traditional passage retrieval

    Researchers have introduced the Long-horizon Memory Embedding Benchmark (LMEB), a new evaluation framework designed to assess the capabilities of embedding models in handling complex, long-horizon memory retrieval tasks…

  4. TOOL · CL_15953 ·

    Causal2Vec enhances decoder-only LLMs for embeddings without architecture changes

    Researchers have introduced Causal2Vec, a novel method to enhance decoder-only large language models (LLMs) for embedding tasks without altering their core architecture. This approach involves pre-encoding input text in…

  5. TOOL · CL_15862 ·

    EPIC training method boosts LLM text encoder performance on MTEB benchmark

    Researchers have developed a new training strategy called EPIC (Embedding-based In-Context Prompt Training) to improve the quality of text embeddings generated by large language models. This method reduces computational…

  6. RESEARCH · CL_01537 ·

    Hugging Face launches MTEB benchmark for Polish text embeddings

    Researchers have introduced the Polish Massive Text Embedding Benchmark (PL-MTEB), a new evaluation suite designed to assess text embedding models specifically for the Polish language. This benchmark includes 30 diverse…

  7. SIGNIFICANT · CL_01566 ·

    OpenAI launches new embedding models with price cuts and performance boosts

    OpenAI has released new embedding models, text-embedding-3-small and text-embedding-3-large, offering significant improvements in performance and efficiency over previous models like text-embedding-ada-002. These new mo…