Scaling Embedding Layers in Language Models

Abstract: We propose SCONE ($S$calable, $C$ontextualized, $O$ffloaded, $N$-gram $E$mbedding), a new method for extending input embedding layers to enhance LLM performance. To avoid increased decoding costs, SCONE retains the original vocabulary while introducing embeddings for a set of frequent $n$-grams. These embeddings provide contextualized representation for each input token and are learned with a separate model during training. After training, embeddings are precomputed and stored in off-accelerator memory; during inference, querying them has minimal impact on latency due to the low complexity of embedding lookups. SCONE enables two new scaling strategies: increasing the number of $n$-gram embeddings and scaling the model used to learn them, both while maintaining fixed accelerator usage during inference (in terms of FLOPS and memory). We show that scaling both aspects enables a model with 1B accelerator-resident parameters to outperform a 1.9B-parameter baseline across diverse corpora, while using only about half the FLOPS and accelerator memory during inference.

• The paper presents Scone, which decouples input embedding transformations from output logits to efficiently scale vocabulary size.
• It employs precomputed, off-accelerator n-gram embeddings to maintain fixed inference-time FLOPS despite larger embedding layers.
• Empirical results demonstrate comparable or superior performance to a 1.9 billion parameter baseline while roughly halving inference FLOPS.

Scaling Embedding Layers in LLMs

The paper "Scaling Embedding Layers in LLMs" presents a method termed Scone (Scalable, Contextualized, Offloaded, N-gram Embedding) for improving the performance of LLMs as embedding layer sizes are increased. This approach is predicated on the insight that expanding vocabulary size in embedding layers has inherent limitations and practical costs, especially concerning inference-time computational demands. Scone circumvents these limitations by decoupling input embedding transformations from output logits computation, allowing for scalable improvements without the traditional increase in inference-time Floating Point Operations per Second (FLOPS).

Key Methodological Insights

Scone introduces new scaling strategies by retaining the base vocabulary and enhancing it with embeddings for selected frequent n-grams. This offers a form of contextualized representation which is learned separately and precomputed for efficient retrieval. These embeddings are stored in off-accelerator memory, thus minimizing the impact on inference speed substantially. By doing so, Scone allows for two primary scaling strategies: increasing the number of cached n-gram embeddings and adjusting the computational resources allocated to learning these embeddings. Importantly, these methods maintain fixed inference-time FLOPS, a significant boon for models where computational costs or latency are deciding factors in deployment viability.

Numerical Results and Empirical Validation

The paper validates the efficacy of the Scone methodology across a range of corpora, demonstrating notable improvements over a 1.9 billion parameter baseline model, achieving comparable or superior performance with approximately half the inference-time FLOPS. Utilizing Scone, the authors achieve these results by optimizing the number of cached n-gram embeddings and the size of the model used to learn these embeddings (the f-gram model). These results underscore a tangible efficiency, allowing users to harness additional computational power during training to bolster model inference capabilities, without increasing runtime costs.

Theoretical and Practical Implications

One of the prominent theoretical implications of this work is a reevaluation of the role of the embedding layer in scaling LLMs. Traditionally, scaling has primarily involved increasing the number of parameters or the depth of layers, with consequent increases in computational demands. Scone adds a dimension where these increases are mitigated, presenting an avenue for achieving higher quality embeddings without proportionate increases in inference resources.

Practically, this offers compelling implications for deploying models in environments constrained by computational budget, such as mobile or edge devices, as well as broader implications for the scalability of serving large models in a cost-effective manner in cloud infrastructure. Scone's approach essentially divides the burden, introducing a novel axis of computational demand management across the training-inference divide.

Future Directions

The directions in which this work could evolve include extending the Scone methodology to other modalities beyond text, such as vision, where embedding layer scaling might similarly alleviate computational bottlenecks. Further exploration could also involve the coupling of Scone with other model optimization techniques such as those involving model sparsity or efficient transformer designs. The application to multilingual models where vocabulary size explosively increases with each additional language is another promising area.

In conclusion, the paper provides a methodologically sound and empirically validated approach to embedding layer scaling, with clear benefits for improving model efficiency in both training and deployment phases. By focusing on intelligent embedding strategies, it sets a precedent for more nuanced future explorations in LLM scaling.