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Rapid Word Learning Through Meta In-Context Learning

Created by
  • Haebom

Author

Wentao Wang, Guangyuan Jiang, Tal Linzen, Brenden M. Lake

Outline

In this paper, we present Minnow (Meta-training for In-context Learning of Words), a novel method for enhancing small-shot word learning. This approach builds on the human ability to rapidly learn new words from a small number of examples and use them flexibly across diverse contexts. Minnow trains a language model to generate examples of new words using special placeholder tokens. The key is to develop general word learning abilities by repeatedly training a diverse set of new words. Experimental results demonstrate that Minnow, trained from scratch on child language data, achieves small-shot word learning performance comparable to that of a large-scale language model (LLM) pre-trained with significantly more data. Furthermore, fine-tuning Minnow on a pre-trained LLM improves the ability to segment new words, identify syntactic categories, and generate new usage examples and definitions. These results demonstrate Minnow's data efficiency and its potential to enhance language model performance in word learning tasks.

Takeaways, Limitations

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Takeaways:
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Demonstrating Minnow's data efficiency in effectively learning new words using small amounts of data.
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Experimentally demonstrating the effectiveness of Minnow in improving the small-shot word learning ability of pre-trained LLMs.
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We observed improved performance on a variety of word learning tasks, including segmenting new words, identifying syntactic categories, and generating new exemplars and definitions.
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Deepening our understanding of human word learning ability and suggesting potential for contributing to the development of language models.
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Limitations:
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In this paper, Minnow's performance was evaluated based on a specific dataset and evaluation indicators, so further research is needed to determine its generalizability to other datasets or indicators.
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Analysis of Minnow's computational costs and training time is lacking. Further research is needed to determine its effectiveness when applied to large-scale datasets.
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Lack of detailed explanation of how Minnow uses placeholder tokens or discussion of Limitations.
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Comparative analysis with other minority-shot word learning methods is limited. Comparative research with other methods is needed to further demonstrate Minnow's superiority.
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