Papers with instruction-tuning training datasets
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models (2024.findings-acl)
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| Challenge: | Instruction tuning language models can be expensive and expensive to train . current methods require extensive training on large datasets, resulting in high training costs. |
| Approach: | They propose a novel approach to selecting training data based on the learning percentage of the samples. |
| Outcome: | The proposed model performs better on models ranging from 1B to 13B in size compared to training on the entire dataset. |