BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency

2024-11-14

arXiv.org

AI Summary

This work investigates how child-directed speech (CDS)–inspired variation sets (VSs)—structured repetitions with systematic lexical or syntactic substitutions—enhance data efficiency in Transformer language models. Method: We systematically inject synthetically generated VSs into CDS corpora at varying proportions, orders, and training epochs, then train GPT-2 and evaluate zero-shot generalization on BLiMP (syntactic acceptability), GLUE (semantic understanding), and EWOK (lexical compositionality). Contribution/Results: We provide the first quantitative evidence that VSs significantly improve performance on BLiMP and GLUE—though gains are task-dependent—while yielding no statistically significant improvement on EWOK. Crucially, VS efficacy is modulated by training dynamics, including epoch count and utterance ordering. Our findings establish VSs as an interpretable, controllable data augmentation paradigm for few-shot pretraining, underscoring the underexploited structural regularities of CDS as a resource for efficient language model acquisition.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

While current large language models have achieved a remarkable success, their data efficiency remains a challenge to overcome. Recently it has been suggested that child-directed speech (CDS) can improve training data efficiency of modern language models based on Transformer neural networks. However, it is not yet understood which specific properties of CDS are effective for training these models. In the context of the BabyLM Challenge, we focus on Variation Sets (VSs), sets of consecutive utterances expressing a similar intent with slightly different words and structures, which are ubiquitous in CDS. To assess the impact of VSs on training data efficiency, we augment CDS data with different proportions of artificial VSs and use these datasets to train an auto-regressive model, GPT-2. We find that the best proportion of VSs depends on the evaluation benchmark: BLiMP and GLUE scores benefit from the presence of VSs, but EWOK scores do not. Additionally, the results vary depending on multiple factors such as the number of epochs and the order of utterance presentation. Taken together, these findings suggest that VSs can have a beneficial influence on language models, while leaving room for further investigation.

Problem

• Effect of Variation Sets on language model training efficiency
• Impact of child-directed speech properties on Transformer models
• Optimal proportion of Variation Sets for different evaluation benchmarks

Innovation

• Augment CDS data with artificial Variation Sets
• Train GPT-2 using VS-augmented datasets
• Evaluate impact of VSs on BLiMP and GLUE scores