Small Language Models Improve Giants by Rewriting Their Outputs

Despite the impressive performance of large language models (LLMs), they often lag behind specialized models in various tasks. LLMs only use a fraction of the existing training data for in-context learning, while task-specific models harness the full dataset for fine-tuning. In this work, we tackle the problem of leveraging training data to improve the performance of LLMs without fine-tuning. Our approach directly targets LLM predictions without requiring access to their weights. We create a pool of candidates from the LLM …

context cs.cl cs.lg data dataset fine-tuning harness in-context learning language language models large language large language models llms performance small small language models tasks training training data work