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.
Sparse, Dense, and Attentional Representations for Text Retrieval
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MEDIAREF is a publicly available knowledge store of documents from 200 media sources that enables low-cost, reproducible evaluation of media background check generation for fact-checking systems.
RSRank learns calibrated relevance scores from alignment between representational shifts induced by candidate documents and those from oracle document sets, enabling zero-threshold filtering.
Prototype RAG virtual assistant integrates Rubin Observatory documentation using Weaviate, LangChain, and GPT for conversational semantic search.
citing papers explorer
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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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Know Your Source: A Public Knowledge Store for Media Background Checks
MEDIAREF is a publicly available knowledge store of documents from 200 media sources that enables low-cost, reproducible evaluation of media background check generation for fact-checking systems.
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RSRank: Learning Relevance from Representational Shifts
RSRank learns calibrated relevance scores from alignment between representational shifts induced by candidate documents and those from oracle document sets, enabling zero-threshold filtering.
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Development of a Retrieval-Augmented Generation Virtual Assistant for Enhanced Information Discovery at Rubin Observatory
Prototype RAG virtual assistant integrates Rubin Observatory documentation using Weaviate, LangChain, and GPT for conversational semantic search.