Ziya2: Data-centric Learning is All LLMs Need
- URL: http://arxiv.org/abs/2311.03301v2
- Date: Thu, 4 Apr 2024 17:41:12 GMT
- Title: Ziya2: Data-centric Learning is All LLMs Need
- Authors: Ruyi Gan, Ziwei Wu, Renliang Sun, Junyu Lu, Xiaojun Wu, Dixiang Zhang, Kunhao Pan, Junqing He, Yuanhe Tian, Ping Yang, Qi Yang, Hao Wang, Jiaxing Zhang, Yan Song,
- Abstract summary: We propose Ziya2, a model with 13 billion parameters adopting LLaMA2 as the foundation model, and further pre-trained on 700 billion tokens.
Experiments show that Ziya2 significantly outperforms other models in multiple benchmarks especially with promising results compared to representative open-source ones.
- Score: 41.44909548662012
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Various large language models (LLMs) have been proposed in recent years, including closed- and open-source ones, continually setting new records on multiple benchmarks. However, the development of LLMs still faces several issues, such as high cost of training models from scratch, and continual pre-training leading to catastrophic forgetting, etc. Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings. In this work, we propose Ziya2, a model with 13 billion parameters adopting LLaMA2 as the foundation model, and further pre-trained on 700 billion tokens, where we focus on pre-training techniques and use data-centric optimization to enhance the learning process of Ziya2 on different stages. We define three data attributes and firstly establish data-centric scaling laws to illustrate how different data impacts LLMs. Experiments show that Ziya2 significantly outperforms other models in multiple benchmarks especially with promising results compared to representative open-source ones. Ziya2 (Base) is released at https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base and https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary.
Related papers
- S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning [51.84977135926156]
We introduce S$2$R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Our results demonstrate that Qwen2.5-math-7B achieves an accuracy improvement from 51.0% to 81.6%, outperforming models trained on an equivalent amount of long-CoT distilled data.
arXiv Detail & Related papers (2025-02-18T13:40:22Z) - A Comprehensive Analysis on LLM-based Node Classification Algorithms [21.120619437937382]
We develop a comprehensive and testbed for node classification using Large Language Models (LLMs)
It includes ten datasets, eight LLM-based algorithms, and three learning paradigms, and is designed for easy extension with new methods and datasets.
We conduct extensive experiments, training and evaluating over 2,200 models, to determine the key settings that affect performance.
Our findings uncover eight insights, e.g., LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting.
arXiv Detail & Related papers (2025-02-02T15:56:05Z) - Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
Large Language Models (LLMs) have been extensively applied in time series analysis.
Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored.
We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
arXiv Detail & Related papers (2025-01-30T03:59:59Z) - Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud [12.651588927599441]
We present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning.
These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs.
Experiments and an application study prove the effectiveness of our approach.
arXiv Detail & Related papers (2024-12-06T09:04:12Z) - Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [71.2981957820888]
We propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets.
The framework initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method.
The generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality.
arXiv Detail & Related papers (2024-11-21T02:30:53Z) - 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering [50.12622877002846]
Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task.
We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs.
Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
arXiv Detail & Related papers (2024-09-24T07:38:38Z) - Achieving Peak Performance for Large Language Models: A Systematic Review [0.0]
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP)
As models grow into the trillion- parameter range, computational and memory costs increase significantly.
This makes it difficult for many researchers to access the resources needed to train or apply these models.
arXiv Detail & Related papers (2024-09-07T13:57:41Z) - Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
We focus on the field of large language models (LLMs) for recommendation.
We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users.
We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
arXiv Detail & Related papers (2024-08-07T04:20:28Z) - LLMaAA: Making Large Language Models as Active Annotators [32.57011151031332]
We propose LLMaAA, which takes large language models as annotators and puts them into an active learning loop to determine what to annotate efficiently.
We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction.
With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples.
arXiv Detail & Related papers (2023-10-30T14:54:15Z) - To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs.
We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting.
Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
arXiv Detail & Related papers (2023-05-22T17:02:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.