Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions
This provides a comprehensive overview for researchers and practitioners in AI/NLP, but it is incremental as it synthesizes existing knowledge without new empirical results.
The paper systematically reviews literature on large language models (LLMs) to identify themes in their development, impacts, and limitations, covering aims, methodologies, and future directions including ethical and societal implications.
There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation of the literature to identify the prominent themes and directions of LLM developments, impacts, and limitations. Our findings illustrate the aims, methodologies, limitations, and future directions of LLM research. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLM development. Overall, this paper provides a rigorous and comprehensive overview of current research in LLM and identifies potential directions for future development. The article highlights the application areas that could have a positive impact on society along with the ethical considerations.