May 19, 2024 · 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 ...
Aug 24, 2024 · In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only performs well on ...
May 19, 2024 · In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only performs well on ...
In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly gen- eralizable parameters that not only performs well on ...
Nov 7, 2024 · In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only ...
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May 20, 2024 · The key idea behind MAML-en-LLM is to train the LLM in a way that makes it better able to quickly adapt to new tasks or datasets, even from limited data.
May 21, 2024 · Our paper titled "MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning" was accepted to #KDD2024!
May 21, 2024 · Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem ...
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 ...
Model agnostic meta-learning (MAML) is one of the most widely used gradient-based meta-learning, consisting of two optimization loops: an inner loop and outer ...