Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across domains . but, for challenging tasks, finetuning often requires substantial human annotations - a process that is time-consuming, labor-intensive, and expensive . |
| Approach: | They propose a method that leverages task-diversity as a principle for effective data selection. |
| Outcome: | The proposed method achieves better accuracy than training on the complete dataset (4% increase in MMLU score). |
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