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