LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

arXiv:2403.15042v1 Announce Type: new
Abstract: Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for …

abstract applications art arxiv boosting cs.cl data fine-tuning iterative language language models language processing large language large language models llms low making natural natural language natural language processing novel performance processing state tasks them type vast world