Summary of Risks, Causes, and Mitigations Of Widespread Deployments Of Large Language Models (llms): a Survey, by Md Nazmus Sakib et al.
Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey
by Md Nazmus Sakib, Md Athikul Islam, Royal Pathak, Md Mashrur Arifin
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper surveys the recent advancements in Large Language Models (LLMs) such as ChatGPT and LLaMA, which have transformed Natural Language Processing (NLP) with their text generation, summarization, and classification abilities. However, the widespread adoption of LLMs introduces challenges related to academic integrity, copyright, environmental impacts, and ethical considerations like data bias, fairness, and privacy. The paper provides an in-depth analysis of the risks associated with specific LLMs, identifying sub-risks, their causes, and potential solutions. LLMs have been used for tasks such as text summarization, classification, and generation, demonstrating impressive capabilities. However, their widespread adoption raises concerns about reliability and generalizability of evaluations. The paper explores the broader challenges related to LLMs, detailing their causes and proposing mitigation strategies. The study aims to deepen understanding of the implications and complexities surrounding these powerful models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at the latest developments in Large Language Models (LLMs) that can generate text, summarize information, and classify things. These super-powerful computers have changed the way we do natural language processing, but they also bring big challenges like cheating, copyright problems, harming the environment, and fairness issues. The study shows what’s good and bad about these models, helping us understand why we should be careful when using them. |
Keywords
» Artificial intelligence » Classification » Llama » Natural language processing » Nlp » Summarization » Text generation
