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Paper
A Survey of Large Language Models
TL;DR
This survey provides a structured, comprehensive roadmap to large language models, tracing their evolution from statistical and neural language models to modern LLMs, with emphasis on scaling, emergent abilities, and practical deployment. It organizes current knowledge around four core areas—pre-training, adaptation tuning, utilization, and capacity evaluation—and couples this with a thorough review of data resources, architectures, and tooling. The authors consolidate resources, protocols, and empirical insights, offering guidance on data quality, curriculum design, efficient training, and alignment, while highlighting ongoing challenges in safety, hallucination, and knowledge recency. The work also surveys applications across NLP, information retrieval, multimodal systems, and domain-specific domains, and points to future directions such as sustained alignment, efficient adaptation, and robust evaluation. By providing a public GitHub repository of resources and experiments, the paper aims to accelerate reproducibility and practical progress in the rapidly evolving field of LLMs.
Abstract
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.