Papers with sparse upcycling
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated considerable proficiency in general natural language processing tasks. |
| Approach: | They propose a parameter-efficient sparsity crafting method which crafts dense models into sparse models using the mixture-of-experts architecture. |
| Outcome: | The proposed method significantly reduces computational costs and GPU memory requirements, while maintaining the quality of approximation in function space. |
Improved Sparse Upcycling for Instruction Tuning (2025.coling-main)
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| Challenge: | Existing methods for sparse upcycling lead to performance degradation in instruction tuning scenarios. |
| Approach: | They propose a representation-based approach to convert dense language models into sparsely activated ones by initializing router weights from language models. |
| Outcome: | The proposed architecture improves model capabilities and routing consistency across multiple benchmarks. |