Road of Large Language Model: Source, Challenge, and Future Perspectives.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Language Models (LMs) are foundational to artificial intelligence, with recent advancements leading to Large Language Models (LLMs).
• LLMs, pretrained on extensive datasets using transformer architectures, exhibit significant zero-shot and few-shot learning abilities.
• Despite their success, current LLMs struggle with complex real-world problem-solving.

Purpose of the Study:

• To provide a historical overview of Language Model development.
• To discuss the current challenges and future directions in LLM research.
• To explore key aspects of LLMs: emergent abilities, human alignment, retrieval augmented generation, and domain-specific applications.

Main Methods:

• Review of existing literature on Language Models and Large Language Models.
• Analysis of transformer-based pretraining methodologies.
• Focus on specific LLM characteristics including emergent abilities, human alignment, RAG, and applications.

Main Results:

• LLMs demonstrate remarkable performance across diverse tasks, driving widespread interest.
• The advent of models like DeepSeek signals continued rapid progress in the field.
• Key areas for advancement include enhancing emergent abilities, ensuring human alignment, and optimizing retrieval augmented generation.

Conclusions:

• LLMs represent a significant leap in AI, with vast potential across various domains.
• Addressing current limitations in real-world applicability is crucial for future LLM development.
• Continued research into emergent abilities, human alignment, RAG, and specialized applications will shape the future of LLMs.