NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.
Massive: A 1m-example multilingual natural language understanding dataset with 51 typologically-diverse languages
3 Pith papers cite this work. Polarity classification is still indexing.
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BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
CUD reshapes the teacher's predictive distribution before distillation so that students receive calibrated uncertainty signals alongside accuracy, yielding more robust and better-calibrated models on high-cardinality and distribution-shift benchmarks.
citing papers explorer
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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
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Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty
CUD reshapes the teacher's predictive distribution before distillation so that students receive calibrated uncertainty signals alongside accuracy, yielding more robust and better-calibrated models on high-cardinality and distribution-shift benchmarks.