A Roadmap for Big Model

TL;DR

This paper provides a comprehensive overview of Big Models (BMs), covering their technologies, prerequisites, applications, and future research directions across multiple domains and topics.

Contribution

It offers the first structured review of BM progress, summarizing key topics and proposing future research directions to guide subsequent work.

Findings

Summarizes 16 BM-related topics and current research status.

Identifies challenges and future directions in BM development.

Provides a holistic view of BM technologies and applications.

Abstract

With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research. In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application. We introduce 16 specific BM-related topics in those four parts, they are Data, Knowledge, Computing System, Parallel Training System, Language Model, Vision Model, Multi-modal Model, Theory&Interpretability, Commonsense Reasoning, Reliability&Security, Governance, Evaluation, Machine Translation, Text Generation, Dialogue and Protein Research. In each topic, we summarize clearly the current studies and propose some future research directions. At the end of this paper, we conclude the further development of BMs in a more general view.