1. Language modeling is a major approach to advancing language intelligence of machines, evolving from statistical language models to neural language models and pre-trained language models (PLMs).
2. Large language models (LLMs) are PLMs of significant size, containing tens or hundreds of billions of parameters, and display surprising emergent abilities that may not be observed in previous smaller PLMs.
3. LLMs have revolutionized the way that humans develop and use AI algorithms, posing a significant impact on the AI community and potentially leading to a prosperous ecosystem of real-world applications based on LLMs.
The article provides a comprehensive survey of large language models (LLMs) and their recent advances. It covers four major aspects of LLMs, including pre-training, adaptation tuning, utilization, and capacity evaluation. The authors highlight the differences between LLMs and pre-trained language models (PLMs) and discuss the emergent abilities of LLMs that are not present in smaller PLMs. They also discuss the impact of LLMs on the AI community and their potential for revolutionizing the way we develop and use AI algorithms.
The article is well-written and provides a thorough review of the literature on LLMs. However, there are some potential biases in the article that should be noted. Firstly, the authors focus mainly on the positive aspects of LLMs and do not discuss their potential risks or negative consequences. While they briefly mention the need for effective control approaches to eliminate potential risks, they do not provide any concrete examples or evidence to support this claim.
Secondly, the authors do not explore counterarguments or alternative perspectives on LLMs. For example, some researchers have raised concerns about the ethical implications of using LLMs for tasks such as automated content generation or decision-making. These concerns are not addressed in the article.
Thirdly, while the authors provide a detailed overview of the technical aspects of LLMs, they do not discuss their social or cultural implications. For example, how might LLMs affect human communication or language use? How might they impact social inequality or bias in language processing?
Overall, while this article provides a useful overview of recent advances in LLM research, it would benefit from a more balanced discussion of both their potential benefits and risks as well as consideration of broader social implications beyond technical advancements.