# A Survey on Large Language Models with some Insights on their Capabilities and Limitations

**Authors:** Andrea Matarazzo, Riccardo Torlone  
**arXiv:** [2501.04040](https://arxiv.org/abs/2501.04040)  
**Date:** 2025-01-03  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2501.04040v1/

## Key points

- Rapid advancement of artificial intelligence, particularly with Large Language Models (LLMs) built on transformer architecture
- LLMs exhibit remarkable performance across various language-related tasks such as text generation, question answering, translation, and summarization
- LLMs have emergent abilities extending beyond core functions like commonsense reasoning, code generation, and arithmetic
- New challenges and critical questions raised about applicability, limitations, and potential for future development of LLMs
- Ethical use and long-term impact of LLMs central to discussions about their future

## Summary

, , , , 

The rapid advancement of artificial intelligence, particularly with the development of Large Language Models (LLMs) built on the transformer architecture, has redefined the capabilities of natural language processing. These models now exhibit remarkable performance across various language-related tasks, such as text generation, question answering, translation, and summarization, often rivaling human-like comprehension. More intriguingly, LLMs have demonstrated emergent abilities extending beyond their core functions, showing proficiency in tasks like commonsense reasoning, code generation, and arithmetic. However, with their increasing complexity and capabilities, these models have introduced new challenges and raised critical questions about their applicability, limitations, and potential for future development. Questions surrounding their ethical use and long-term impact not only to the AI landscape but also to our own lives have become central to discussions about their future. Addressing these concerns is critical as researchers and practitioners continue to explore the transformative possibilities that LLMs can offer. The goal of this paper is twofold. Firstly, it aims to provide an in-depth survey on LLMs and their applications by exploring foundational components such as pre-training strategies and architectural variations. The progression from early language models to sophisticated architectures like BERT,GPT,and LLaMA will be examined along with scaling laws that contribute to understanding how model size impacts performance. The paper will also investigate LLM applications across sectors like healthcare, finance, education,law highlighting adaptability and potential solutions for domain-specific challenges. Secondly,the paper seeks to deepen mechanisms enabling LLMs to perform previously impossible tasks by addressing fundamental questions about learning processes across tasks/domains and factors contributing to emergent abilities. It aims to investigate limitations of these models while focusing on generalization abilities for autonomous task execution. Section 2 introduces LLMs tracing development from statistical language models to transformer-based architectures emphasizing scaling laws' role in enhancing performance across language tasks. Prominent families like BERT,GPT series,Llama are highlighted alongside transformative impacts across domains including healthcare finance education law scientific research.