Speakers
Description
The rapid advancement of artificial intelligence (AI) has brought forth an array of powerful large language models (LLMs) designed to process and generate human-like text across diverse applications. This study provides a comprehensive comparative analysis of leading LLMs, including GPT series, BERT derivatives, and other cutting-edge models such as PaLM and LLaMA. The comparison is conducted on various dimensions, including architectural design, training methodologies, data scaling, computational requirements, performance metrics, and adaptability to downstream tasks. Key benchmarks, such as natural language understanding (NLU), text generation quality, and fine-tuning capabilities, are evaluated. Additionally, the study examines the models’ limitations, such as ethical concerns, computational costs, and susceptibility to bias. Insights into their deployment in real-world applications, including content generation, code synthesis, and conversational AI, are highlighted. The findings aim to guide researchers, developers, and industry professionals in selecting and optimizing LLMs for specific use cases while identifying gaps and future directions for innovation. By delineating the strengths and weaknesses of each model, this study contributes to the broader understanding of the state-of-the-art in LLM research and its implications for advancing AI capabilities responsibly.
| Paper Language | English |
|---|---|
| Contribution Type | Virtual |
