Summary of A Systematic Investigation Of Learnability From Single Child Linguistic Input, by Yulu Qin et al.
A systematic investigation of learnability from single child linguistic input
by Yulu Qin, Wentao Wang, Brenden M. Lake
First submitted to arxiv on: 12 Feb 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 Language models have made significant progress in generating coherent text, sparking discussions about their relevance to understanding human language learnability. However, there is a gap between the training data for these models and child-directed speech. Our research addresses this discrepancy by training language models on subsets of a single child’s linguistic input. Previously, Wang et al. found that LMs trained in this setting can form syntactic and semantic word clusters and develop sensitivity to certain linguistic phenomena using LSTMs and simpler neural networks. In this study, we systematically train six different model architectures on five datasets (three single-child and two baselines) to examine the robustness of learnability from single-child input. Our results show that models trained on single-child datasets consistently matched previous work, underscoring the robustness of forming meaningful syntactic and semantic representations from a subset of a child’s linguistic input. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well language models can understand human language when they’re trained using data from just one child. Language models are good at generating text that makes sense, but right now they’re mostly trained using huge amounts of text data that isn’t like the kind of speech a child hears. The researchers wanted to see if training models on smaller datasets of a single child’s speech would help them understand language better. They tested six different types of models on five different sets of data and found that the models all did well when trained using single-child data. This shows that these models can learn important patterns in language from just one person’s speech. |
