Papers by Naoki Asaba

1 papers
Efficient Vocabulary Reduction for Small Language Models (2025.coling-industry)

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Challenge: Large language models (LLMs) have high computational costs and energy consumption, making their deployment in industrial settings difficult.
Approach: They propose a small language model that compresses the embedding layer and reduces model size without significant loss of performance.
Outcome: The proposed model reduces the embedding layer while maintaining performance while improving accuracy and performance.

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