MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
<|reference_start|>MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning: Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts well to unseen tasks, multiple meta-training approaches have been proposed such as MetaICL and MetaICT, which involve meta-training pre-trained LLMs on a wide variety of diverse tasks. These meta-training approaches essentially perform in-context multi-task fine-tuning and evaluate on a disjointed test set of tasks. Even though they achieve impressive performance, their goal is never to compute a truly general set of parameters. In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only perform well on disjointed tasks but also adapts to unseen tasks. We see an average increase of 2% on unseen domains in the performance while a massive 4% improvement on adaptation performance. Furthermore, we demonstrate that MAML-en-LLM outperforms baselines in settings with limited amount of training data on both seen and unseen domains by an average of 2%. Finally, we discuss the effects of type of tasks, optimizers and task complexity, an avenue barely explored in meta-training literature. Exhaustive experiments across 7 task settings along with two data settings demonstrate that models trained with MAML-en-LLM outperform SOTA meta-training approaches.<|reference_end|>
@article{sinha2024maml-en-llm:,
    title={MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved
  In-Context Learning},
    author={Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni, Jianhua Lu,
  Aidong Zhang},
    journal={arXiv preprint arXiv:2405.11446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11446},
    primaryClass={cs.CL cs.LG}
}