Meet KaLM-Embedding: A Series of Multilingual Embedding Models Built on Qwen2-0.5B and Released Under MIT

The use of multiple languages ​​and cross-linguistic functions is the basis of natural language processing (NLP) today, making robust embedded models essential. These models support systems such as augmented-recovery generation and other AI-driven solutions. However, existing models often struggle with noisy training data, limited domain diversity, and poor performance in handling multilingual datasets. These limitations affect performance and scalability. Researchers from the Harbin Institute of Technology (Shenzhen) have tackled these challenges with KaLM-Embedding, a model that emphasizes data quality and innovative training methods.

KaLM-Embedding is a multilingual embedding model built on Qwen 2-0.5B and released under the MIT license. Designed with compactness and efficiency in mind, it is best suited for real-world applications where computing resources are constrained.

Data-centric model design is a key strength. It includes 550,000 synthetic data samples generated using human-based techniques to ensure diversity and consistency. Additionally, it uses consistency level filtering to remove noisy and bad samples, which improves the quality and robustness of the training data.

Technical Features and Benefits

KaLM-Embedding includes advanced methods to deliver robust text embedding in multiple languages. A notable feature is Matryoshka Representation Learning, which supports variable embedding rates. This flexibility allows embedding to be optimized for different applications, from 64 to 896 pixels.

The training strategy has two phases: unsupervised pre-training and supervised fine-tuning. More than 70 different datasets were used during optimization, including different languages ​​and backgrounds. Combining the semi-homogeneous task further improved the training process by balancing the challenges posed by in-batch inaccuracies and the risk of false negatives.

KaLM-Embedding also benefits from its Qwen 2-0.5B foundation, a pre-trained language model. This structure enables efficient adaptation and embedding of tasks, providing an advantage over traditional BERT-like models.

Performance and Measurement Results

The performance of KaLM-Embedding was evaluated with the Massive Text Embedding Benchmark (MTEB). It received an average score of 64.53, ranking high for models with less than 1 billion parameters. Scores of 64.13 in Chinese-MTEB and 64.94 in English-MTEB highlight its multilingual abilities. Despite limited fine-tuning data for some languages, the model has shown strong generalization abilities.

Ablation studies have provided additional information. Features such as Matryoshka Representation Learning and consistency level filtering have been shown to improve performance. However, the study also highlighted areas for improvement, such as refining low-dimensional embeddings to improve efficiency.

Conclusion: A Step Forward in Multilingual Embedding

KaLM-Embedding represents a major advance in multilingual embedding models. By addressing challenges such as noisy data and inconsistent structures, it achieves a balance between efficiency and effectiveness. An open source release under the MIT license invites researchers and practitioners to explore and build upon this work.

With its strong multilingual functionality and innovative methods, KaLM-Embedding is well-positioned to be used in a variety of ways, from retrieval-augmented systems to cross-language tasks. As the demand for multilingual NLP solutions continues to grow, KaLM-Embedding serves as a testament to the impact of high-quality data and thoughtful design of models.

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