Papers with sparse merging paradigm
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression (2025.findings-emnlp)
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| Challenge: | Large language models excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. |
| Approach: | They propose a Data and Model Compression Framework that categorizes data filtering methodologies into three distinct paradigms: (1) distribution-aware methods, (2) quality-a aware methods, and (3) hybrid approaches considering both dimensions. |
| Outcome: | The proposed framework can select the optimal LLM while saving approximately 20-fold in training time. |