BAAI collaborates with researchers from the University of Science and Technology of China to introduce BGE M3-Embedding. The model addresses limitations in existing text embedding models, supporting over 100 languages, multiple retrieval functionalities, and various input lengths. It outperforms baseline methods and presents a significant advancement in information retrieval. [49 words]
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BAAI introduces BGE M3-Embedding
BAAI introduces BGE M3-Embedding with the help of researchers from the University of Science and Technology of China. The M3 refers to three novel properties of text embedding- Multi-Lingual, Multi-Functionality, and Multi-Granularity. It identifies the primary challenges in the existing embedding models, like being unable to support multiple languages, restrictions in retrieval functionalities, and difficulty handling varied input granularities.
Existing Challenges and Proposed Solution
Existing embedding models, such as Contriever, GTR, E5, and others, have been proven to bring notable progress in the field, but they lack language support, multiple retrieval functionality, or long input texts. The proposed solution, BGE M3-Embedding, supports over 100 languages, accommodates diverse retrieval functionalities (dense, sparse, and multi-vector retrieval), and processes input data ranging from short sentences to lengthy document handling up to 8192 tokens.
Novel Approaches and Evaluation
M3-Embedding involves a novel self-knowledge distillation approach, optimizing batching strategies for large input lengths, for which researchers used large-scale, diverse multi-lingual datasets from various sources like Wikipedia and S2ORC. It facilitates three common retrieval functionalities: dense retrieval, lexical retrieval, and multi-vector retrieval. The model is evaluated for its performance with multilingual text(MLDR), varied sequence length, and narrative QA responses using the nDCG@10 evaluation metric.
Advancements and Effectiveness
In conclusion, M3 embedding is a significant advancement in text embedding models. It is a versatile solution that supports multiple languages, varied retrieval functionalities, and different input granularities. The proposed model addresses crucial limitations in existing methods, marking a substantial step forward in information retrieval. It outperforms baseline methods like BM25, mDPR, and E5, showcasing its effectiveness in addressing the identified challenges.
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