LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering (2025.findings-emnlp)
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| Challenge: | Large Language Models excel at few-shot learning but their direct application in real-world scenarios is often hindered by their high computational cost. |
| Approach: | They propose a framework that uses Large Language Models for data generation and scoring to improve encoder model performance. |
| Outcome: | The proposed approach improves accuracy from 28.9% to 39.3% on a few-shot MCQA task . |
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