Summary of Maml-en-llm: Model Agnostic Meta-training Of Llms For Improved In-context Learning, by Sanchit Sinha et al.
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
by Sanchit Sinha, Yuguang Yue, Victor Soto, Mayank Kulkarni, Jianhua Lu, Aidong Zhang
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors propose a novel method for meta-training large language models (LLMs) called MAML-en-LLM. The goal is to learn truly generalizable parameters that adapt well to unseen tasks without fine-tuning. Multiple existing methods, such as MetaICL and MetaICT, involve in-context multi-task fine-tuning but do not aim to compute truly general set of parameters. In contrast, MAML-en-LLM achieves impressive performance on both seen and unseen domains, with an average increase of 2% on unseen domains and a massive 4% improvement on adaptation performance. The authors also explore the effects of task complexity, optimizers, and type of tasks on model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to train large language models called MAML-en-LLM. The goal is to make these models better at learning new tasks without needing more training data. Right now, there are other methods that can do this, like MetaICL and MetaICT, but they don’t try to learn truly generalizable parameters. Instead, MAML-en-LLM achieves great results on both old and new tasks, with a 2% boost on new domains and a 4% improvement on adapting to new tasks. The researchers also tested how different things like task complexity and the optimizer used affect model performance. |
Keywords
» Artificial intelligence » Fine tuning » Multi task
