Mar 22, 2024 · This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to ...
Aug 11, 2024 · To address this, we propose LLM2LLM, a tar- geted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed ...
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [Paper] ... This is the code for the LLM2LLM paper. Reproducing Main Experiments. We have provided ...
People also search for
LLM2LLM relies on using LLMs to augment a training dataset in order to train another student LLM more efficiently. This can reduce the energy and monetary cost ...
Mar 25, 2024 · This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to ...
Mar 22, 2024 · This work proposes LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by ...
data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a tar- geted and iterative data augmentation strategy that uses a teacher ...
Apr 1, 2024 · LLM2LLM seems to reduce the dependence on labor-intensive data curation to pave the path for more scalable and, higher performance LLMs, being ...
Sep 23, 2024 · (2024) , the authors propose an adaptive procedure where a large language model is used to generate targeted fine-tuning data for a small model ...
Mar 25, 2024 · LLM2LLM Boosting LLMs with Novel Iterative Data Enhancement Pretrained large language models (LLMs) are currently state-of-the-art for ...