Summary of Decoding Large-language Models: a Systematic Overview Of Socio-technical Impacts, Constraints, and Emerging Questions, by Zeyneb N. Kaya and Souvick Ghosh
Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions
by Zeyneb N. Kaya, Souvick Ghosh
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A systematic investigation is conducted to identify prominent themes, limitations, and future directions of large language model (LLM) developments, impacts, and limitations in natural language processing (NLP) and artificial intelligence (AI). Findings illustrate aims, methodologies, limitations, and potential directions for LLM research, including responsible development considerations, algorithmic improvements, ethical challenges, and societal implications. The paper provides a comprehensive overview of current LLM research and highlights application areas with positive impact on society and ethical considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are revolutionizing natural language processing and artificial intelligence. This study looks at what’s happening in the field, what’s working well, and what’s not. It talks about how these models can be used to help people, but also raises important questions about ethics and responsibility. The goal is to give a clear picture of where we are now and where we might go from here. |
Keywords
» Artificial intelligence » Large language model » Natural language processing » Nlp
