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Summary of The Human Factor in Detecting Errors Of Large Language Models: a Systematic Literature Review and Future Research Directions, by Christian A. Schiller


The Human Factor in Detecting Errors of Large Language Models: A Systematic Literature Review and Future Research Directions

by Christian A. Schiller

First submitted to arxiv on: 13 Mar 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); 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
The introduction of ChatGPT by OpenAI has revolutionized the field of Artificial Intelligence, bringing Large Language Models (LLMs) to the forefront and achieving unprecedented user adoption. These models, particularly ChatGPT, have been trained on vast amounts of internet data, showcasing impressive conversational capabilities across diverse domains. As a result, they are poised to significantly impact the workforce. However, these models are prone to errors – “hallucinations” and omissions – generating incorrect or incomplete information. This presents significant risks, especially in contexts where accuracy is paramount, such as legal compliance, medicine, and fine-grained process frameworks.
Low GrooveSquid.com (original content) Low Difficulty Summary
ChatGPT, a new AI model, has taken the world by storm! It’s like a super-smart chatbot that can talk about lots of things. People are using it to do all sorts of tasks, from answering questions to generating text. But some experts are worried because this model is not always accurate – it might give you false or incomplete information. This could be a problem in important areas like law, medicine, and business. We’ll have to see how this technology develops.

Keywords

* Artificial intelligence