OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch

TL;DR

This report presents OpenBA, an open-sourced 15B bilingual asymmetric Seq2Seq model, to contribute an LLM variant to the Chinese-oriented open-source model community and enhances OpenBA with effective and efficient techniques.

Abstract

Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model, to contribute an LLM variant to the Chinese-oriented open-source model community. We enhance OpenBA with effective and efficient techniques as well as adopt a three-stage training strategy to train the model from scratch. Our solution can also achieve very competitive performance with only 380B tokens, which is better than LLaMA-70B on the BELEBELE benchmark, BLOOM-176B on the MMLU benchmark, GLM-130B on the C-Eval (hard) benchmark. This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques. Additionally, we also provide the fine-tuning details of OpenBA on four downstream tasks. We have refactored our code to follow the design principles of the Huggingface Transformers Library, making it more convenient for developers to use, and released checkpoints of different training stages at https://huggingface.co/openBA. More details of our project are available at https://github.com/OpenNLG/openBA.git.

Figures (8)

• Figure 1: The composition of Data collection. Figure (a) represents the composition ratio of the pre-training dataset. Figure (b) represents the composition of the Bilingual Flan dataset. Figure (c) represents the finer-grained composition of the Chinese Flan dataset.
• Figure 2: Overview of training process.
• Figure 3: Loss curves for each training stage.
• Figure 4: Human evaluation results on the ROC dataset.
• Figure 5: The performance in terms of loss and accuracy of the three model configurations across four denoising tasks. The first row of figures illustrates the loss performance, while the second row depicts the accuracy. The four columns respectively represent the four tasks: R-Denoising, S-Denoising, X-Denoising, and a combination of the three.