Pre-trained Large Language Models (LLMs) have shown success in a diverse set of language inference and understanding tasks. The pre-training stage of LLMs looks at a large corpus of raw textual data. The BabyLM shared task compares LLM pre-training to human language acquisition, where the number of tokens seen by 13-year-old kids is magnitudes smaller than the number of tokens seen by LLMs. In this work, we pre-train and evaluate LLMs on their ability to learn contextual word representations using roughly the same number of tokens as seen by children. We provide a strong set of baselines; with different architectures, evaluation of changes in performance across epochs, and reported pre-training metrics for the strict small and strict tracks of the task. We also try to loosely replicate the RoBERTa baseline given by the task organizers to observe the training robustness to hyperparameter selection and replicability. We provide the submission details to the strict and strict-small tracks in this report.

翻译：预训练大语言模型（LLMs）已在多种语言推理和理解任务中展现出成功。LLMs的预训练阶段需处理大规模原始文本数据语料库。BabyLM共享任务将LLM预训练与人类语言习得进行对比，其中13岁儿童接触的令牌数量远小于LLMs处理的令牌数。本研究中，我们使用与儿童大致相同数量的令牌预训练并评估LLMs学习上下文词表示的能力。我们提供一组强大的基线：涵盖不同架构、评估各训练周期性能变化，并报告严格小规模与严格规模任务的预训练指标。我们还尝试松散复现任务组织者提供的RoBERTa基线，以观察训练对超参数选择的鲁棒性与可复现性。本报告提交了严格规模与严格小规模轨道的详细结果。