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The article offers an opinion regarding how a human-artificial intelligence (AI) collaboration can asses relevance. Topics include the improvements to large language models (LLMs) such as OpenAI's chatbot ChatGPT, the reasons why utilizing only LLMs can be problematic, and how human judgement can improve fairness, efficiency, and effectiveness.

This research article focuses on the science of detecting large language model (LLM) generated text. The authors discuss the advancement of natural language generation (NLG) technology, including OpenAI's ChatGPT, and explain how the two detection methods, black-box detection and white-box detection, work to mitigate the potential misuse of LLMs.

The article discusses the performance of large language models (LLMs) like ChatGPT in languages other than English, highlighting challenges and potential solutions. Researchers found that LLMs struggle to mimic humans in languages like Japanese due to differences in syntax and writing styles, leading to concerns about their effectiveness in non-English languages. Efforts to improve LLM performance in other languages include innovative tokenization methods and leveraging search data to supplement training data, aiming to address the scarcity of language-specific training resources.

The article provides the author's perspective on the reliability of the ChatGPT, which is an artificially intelligent (AI) chatbot that derives from a large language model (LLM), in the communication of science. Particular focus is given to the errors that ChatGPT encounters when communicating scientific knowledge to a general audience.

The article focuses on how large language models (LLMs) are influencing online communities. The authors offer their opinions, stating that generative artificial intelligence (AI) technologies, such as ChatGPT, are causing a decrease in user participation of knowledge communities and degrading the quality of answers the community provides.

This article presents generative artificial intelligence (AI) as a potential new innovation platform. First this article provides the history of generative AI with a discussion of Microsoft, OpenAI, and the use of neural networks and large language models. Next, a look at whether generative AI could meet the criteria of an innovation platform. Lastly, a discussion of regulation and governance.

This article examines the use of generative artificial intelligence (AI), namely OpenAI’s ChatGPT, with an emphasis on trust as well as its impact on education and jobs. The author discusses limitations to ChatGPT and how these can lead to errors in its responses but also highlights ways chatbots can be used to assist in a professional’s work.

The article discusses the collaboration between Senior Editor Ralph Raiola and OpenAI's ChatGPT to create an original sci-fi short story. The article provides a full transcript of the collaborative process and a partially finished product.

This article explores the idea of an international agency for the control of artificial intelligence (AI). Topics include the author’s experience speaking up about this need at a United States Senate hearing and in other articles and publications, the concerns around a government lead regulatory body for AI, and two hypothetical ways this regulation of AI could play out.

The article discusses the burgeoning of artificial intelligent chatbots such as ChatGPT and the threats that these tools can be used for malicious purposes such as phishing attacks, harassment, plagiarism and fake news. New detectors known as classifiers have been developed to distinguish between human-written and computer-generated text to help mitigate some of these issues.

The article focuses on the challenges of responsibility when it comes to artificial intelligence (AI). The author discusses the generative text programming known as ChatGPT, the "Blueprint for an AI Bill of Rights" put forth by the U.S. Office of Science and Technology Policy (OSTP), and the Association for Computing Machinery (ACM) policy statement.