Ziya2: A Data-Centric Approach to Enhance Large Language Models' Capabilities in Chinese, Mathematics, and Programming

Core Concepts

Ziya2, a 13-billion-parameter language model, is developed through a data-centric approach that focuses on optimizing the use of pre-training data to enhance the model's capabilities in Chinese, mathematics, and programming tasks, while maintaining or improving its performance on general English benchmarks.

Abstract

The paper presents the development of Ziya2, a 13-billion-parameter large language model (LLM) that builds upon the open-source LLaMA2 model. The key highlights are: Data Processing Pipeline: The authors propose a comprehensive data processing pipeline that includes data preprocessing, automatic scoring, rule-based filtering, content deduplication, and data evaluation. This pipeline is used to clean and curate a high-quality pre-training dataset exceeding 700 billion tokens, covering English, Chinese, and multilingual data. Continual Pre-training Strategy: The authors adopt a three-stage continual pre-training strategy, where the first stage uses unsupervised data, the second stage incorporates supervised datasets, and the third stage focuses on improving mathematical abilities. This strategy aims to enhance the model's capabilities in Chinese, mathematics, and programming, while maintaining or improving its performance on general English benchmarks. Model Improvements: The authors make several structural improvements to the LLaMA2 architecture, including the tokenizer, positional embedding, layer normalization, and attention mechanisms, to better adapt to the diverse data distribution and improve training efficiency. Benchmark Evaluation: Ziya2 is evaluated on six representative benchmarks, including MMLU, CMMLU, C-Eval, GSM8K, MATH, and HumanEval. The results show that Ziya2 significantly outperforms LLaMA2 and other open-source models of comparable size, especially in Chinese, mathematical, and programming tasks. Data-centric Scaling Laws: The authors define three data attributes (Coherence, Readability, and Similarity) and establish data-centric scaling laws to illustrate the impact of different data characteristics on LLM performance. The findings suggest that improving the semantic and grammatical quality of pre-training data is more effective in enhancing model performance than data augmentation. Overall, the Ziya2 model demonstrates the effectiveness of the proposed data-centric approach in developing large language models with enhanced capabilities across multiple domains.

Stats

The pre-training dataset for Ziya2 exceeds 700 billion tokens, covering English, Chinese, and multilingual data. The dataset includes Pile-Pajama (110B tokens), CC (109B tokens), Wudao (48B tokens), Yuan1.0 (193B tokens), Translate (1.5B tokens), Code (191B tokens), Instruct (0.8B tokens), Wanjuan (29B tokens), and MetaMath (0.1B tokens).

Quotes

"Ziya2 significantly outperforms LLaMA2 on all the benchmarks. Specifically, for general English tasks, Ziya2 outperforms LLaMA2 by 6 points on MMLU. For general Chinese tasks, Ziya2 surpasses LLaMA2 by 23 and 24 points on CMMLU and C-Eval, respectively. For specific downstream tasks, Ziya2 outperforms LLaMA2 by 40, 6, and 13 points on GSM8K, MATH, and HumanEval datasets, respectively." "The results highlight the effectiveness of our continual pre-training strategy. It not only enhances LLaMA2's English capabilities and mitigates catastrophic forgetting but also significantly improves its performance in Chinese, mathematical, and code programming tasks."

Key Insights Distilled From

Ziya2

by Ruyi Gan,Ziw... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2311.03301.pdf

Deeper Inquiries

How can the data-centric scaling laws established in this work be applied to guide the development of other large language models beyond Ziya2

The data-centric scaling laws established in this work can serve as a valuable framework for guiding the development of other large language models beyond Ziya2. By understanding the intricate relationship between data attributes and model performance, researchers can optimize the use of pre-training data in a cost-effective manner. This approach can help in determining which data attributes have the most significant impact on LLMs and prioritize them accordingly. By defining and establishing data-centric scaling laws, researchers can ensure that the quality and coherence of the data play a crucial role in enhancing the capabilities of large language models. This methodology can be applied to future LLM development by focusing on improving data quality, coherence, and readability to achieve better model performance across various tasks and benchmarks.

What are the potential limitations or drawbacks of the data-centric approach proposed in this work, and how could they be addressed in future research

While the data-centric approach proposed in this work offers significant benefits in enhancing the performance of large language models like Ziya2, there are potential limitations and drawbacks that need to be considered. One limitation could be the scalability of the approach to even larger models with more parameters. As model sizes increase, the computational resources required for data-centric optimization may become prohibitive. Additionally, the human evaluation process for data quality may be subjective and time-consuming, potentially limiting the scalability of the approach. To address these limitations, future research could focus on developing automated methods for data quality assessment and scaling the data-centric approach to accommodate larger models efficiently. Moreover, ensuring the generalizability of the data-centric approach across different languages and domains will be crucial for its widespread adoption in LLM development.

Given the significant improvements in Chinese, mathematical, and programming capabilities demonstrated by Ziya2, how might this model be leveraged to advance applications in fields such as scientific computing, software engineering, or education

The significant improvements demonstrated by Ziya2 in Chinese, mathematical, and programming capabilities open up various opportunities for applications in fields such as scientific computing, software engineering, and education. In scientific computing, Ziya2's enhanced mathematical abilities can be leveraged for complex calculations, simulations, and data analysis tasks. The model's proficiency in programming tasks can be beneficial for automating code generation, debugging, and software development processes in software engineering. In the education sector, Ziya2 can be utilized for creating interactive learning platforms, generating educational content, and providing personalized tutoring in subjects like mathematics and programming. By integrating Ziya2 into these applications, researchers and developers can enhance efficiency, accuracy, and innovation in diverse domains, ultimately advancing the capabilities of AI systems in real-world scenarios.