Summary of Bge M3-embedding: Multi-lingual, Multi-functionality, Multi-granularity Text Embeddings Through Self-knowledge Distillation, by Jianlv Chen et al.
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
by Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents M3-Embedding, a versatile embedding model that excels in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It supports over 100 languages, achieving state-of-the-art performances on multi-lingual and cross-lingual retrieval tasks. The model can perform dense, multi-vector, and sparse retrievals simultaneously, making it a unified foundation for real-world IR applications. It processes inputs of varying granularities, from short sentences to long documents up to 8192 tokens. To train M3-Embedding effectively, the authors propose self-knowledge distillation, integrating relevance scores from different retrieval functionalities as a teacher signal to enhance training quality. They also optimize batching strategies for large batch sizes and high training throughput. As far as we know, M3-Embedding is the first model achieving such versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary M3-Embedding is a new way of understanding languages that can help us better communicate with each other across different cultures. It’s like having a special key to unlock all languages, making it easier for computers to understand and find information in any language. The authors also developed a new way of training this model using something called self-knowledge distillation. This helps the model learn from its own strengths and weaknesses, making it even better at understanding different languages. |
Keywords
* Artificial intelligence * Embedding * Knowledge distillation
