SEA-Embedding is a fully open text embedding pipeline for Southeast Asian languages that achieves state-of-the-art performance on the SEA-BED benchmark by analyzing data composition, training objectives, and base encoder choices.
MIRACL : A Multilingual Retrieval Dataset Covering 18 Diverse Languages
7 Pith papers cite this work, alongside 40 external citations. Polarity classification is still indexing.
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M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
SHIFT mitigates language bias in MLIR by subtracting estimated relative language vectors from document embeddings during indexing using parallel translation pairs.
XBCP benchmark shows deep research agents and multilingual retrievers lose accuracy, recall, calibration, and citation reliability when evidence is in non-English languages, even with gold evidence provided.
Optimal interpolation of query embeddings from parallel translations outperforms the best monolingual query in 88/105 cases on mMARCO, showing English-driven asymmetry and negative correlation with typological distance.
STORM trains lexical query rewriters via reward-guided beam search that converts retrieval metrics into stepwise token signals, enabling 0.6B-8B models to rival dense retrievers on TREC, BEIR and MIRACL without index changes.
Supervised models using embeddings like jina and e5 reach up to 92% accuracy on multilingual hate speech detection, substantially outperforming anomaly detection, while PCA to 64 dimensions preserves most performance in the supervised case.
citing papers explorer
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SEA-Embedding: Open and Reproducible Text Embeddings for Southeast Asia
SEA-Embedding is a fully open text embedding pipeline for Southeast Asian languages that achieves state-of-the-art performance on the SEA-BED benchmark by analyzing data composition, training objectives, and base encoder choices.
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M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
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SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval
SHIFT mitigates language bias in MLIR by subtracting estimated relative language vectors from document embeddings during indexing using parallel translation pairs.
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Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
XBCP benchmark shows deep research agents and multilingual retrievers lose accuracy, recall, calibration, and citation reliability when evidence is in non-English languages, even with gold evidence provided.
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When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
Optimal interpolation of query embeddings from parallel translations outperforms the best monolingual query in 88/105 cases on mMARCO, showing English-driven asymmetry and negative correlation with typological distance.
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STORM: Stepwise Token Optimization with Reward-Guided Beam Search
STORM trains lexical query rewriters via reward-guided beam search that converts retrieval metrics into stepwise token signals, enabling 0.6B-8B models to rival dense retrievers on TREC, BEIR and MIRACL without index changes.
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Comparison of Modern Multilingual Text Embedding Techniques for Hate Speech Detection Task
Supervised models using embeddings like jina and e5 reach up to 92% accuracy on multilingual hate speech detection, substantially outperforming anomaly detection, while PCA to 64 dimensions preserves most performance in the supervised case.