SCONE: Scaling Language Model Embeddings Efficiently

Top post
Efficient Scaling of Embedding Layers in Language Models
The ever-increasing size of language models enables impressive progress in natural language processing. At the same time, scaling these models, especially the embedding layers, poses a challenge. Larger vocabularies and contextualized embeddings lead to increased computational effort and memory requirements, which negatively impacts inference speed. An innovative approach to address this issue is SCONE (Scalable, Contextualized, Offloaded, N-gram Embedding).
SCONE: A New Approach for Efficient Scaling
SCONE allows the expansion of embedding layers without significantly increasing inference costs. In contrast to conventional methods that expand the vocabulary, SCONE maintains the original vocabulary and introduces additional embeddings for frequently occurring N-grams. These N-gram embeddings provide a contextualized representation for each input token and are learned during training with a separate model.
The key advantage of SCONE lies in the offloading of the N-gram embeddings. During inference, these are pre-computed and stored in off-accelerator memory. This minimizes the impact on inference speed and reduces memory requirements on the accelerator. This approach enables two new scaling strategies: increasing the number of cached N-gram embeddings and scaling the model used to learn these embeddings. Both strategies can be applied without increasing the FLOPS (Floating Point Operations Per Second) during inference.
Performance and Efficiency
Studies show that SCONE achieves significantly improved performance compared to conventional methods. By scaling the number of N-gram embeddings and the training model, SCONE outperforms a 1.9 billion parameter baseline on various corpora while requiring only half the FLOPS during inference. This demonstrates the efficiency of SCONE in scaling embedding layers in large language models.
Contextualized Representations and Reduced Computational Effort
The use of N-gram embeddings in SCONE allows for a more nuanced and contextualized representation of words and phrases. This enables the model to better capture the meaning of words in context and thus improve the accuracy of predictions. At the same time, offloading the embeddings reduces the computational effort during inference, leading to faster processing and lower latency.
Future Prospects
SCONE represents a promising approach for the efficient scaling of embedding layers in language models. The combination of contextualized representations and reduced computational effort enables the development of more powerful language models that are also more resource-efficient. Future research could focus on optimizing N-gram selection and developing even more efficient offloading strategies. This could lead to further improvements in the performance and scalability of language models and open up new possibilities for applications in natural language processing.
Bibliography: Yu, D., Cohen, E., Ghazi, B., Huang, Y., Kamath, P., Kumar, R., Liu, D., & Zhang, C. (2025). Scaling Embedding Layers in Language Models. *arXiv preprint arXiv:2502.01637*. Zhang, C., Han, X., Zhou, C., Xu, M., & Li, C. (2022). Pointer Value Retrieval: A new benchmark for understanding the limits of neural retrieval. *Proceedings of the 39th International Conference on Machine Learning*, *162*, 26275-26298. Khandelwal, U., Schumann, A., Curtis, S., Hosseini, M. J., Kim, M., Goyal, N., ... & Zettlemoyer, L. (2024). FiD: Fusion-in-Decoder Enhanced Language Models. *Findings of the Association for Computational Linguistics: EMNLP 2024*, 1912-1931. Shazeer, N. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. *E17-2025*. Shazeer, N. M., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. *arXiv preprint arXiv:1701.06538*. Izacard, G., Grave, E., Lample, G., & Joulin, A. (2024). Blockwise Parallel Decoding for Large Language Models. *arXiv preprint arXiv:2406.04165*. Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2024). QLoRA: Efficient Finetuning of Quantized LLMs. *arXiv preprint arXiv:2409.07787*. Stack Overflow. (9. November 2023). An intuitive introduction to text embeddings | Stack Overflow Blog. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. *Journal of Machine Learning Research*, *21*(140), 1-67. Kasneci, E., Bui, T., Albert, P., Westenberger, I., Biesalski, E., & Stengel, I. (2024). Large Language Models: How Much Data Do We Need to Achieve an Open-Domain Question Answering Performance Comparable to Humans?. *arXiv preprint arXiv:2409.07847*.