ChatGPT and Other Large Language Models Are Double-edged Swords.
By Yiqiu Shen, L. Heacock, Jonathan Elias, K. Hentel, B. Reig, G. Shih, Linda Moy. Radiology, 2023
895 citations recorded by the source corpus.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/621b8c4b37d1e22d440262dda2206ac13da6d779
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In Health Informatics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity (2023, background): “On the other hand, there has also been as much sharing of its failures in simple tasks (Gilson et al., 2022; Choi et al., 2023; Shen et al., 2023).”
- Large language models in health care: Development, applications, and challenges (2023, methodology): “LLMs can serve as clinical decision support systems to guide physicians to select the most appropriate radiological investigations in specific clinical scenarios [18].”
- Detecting hallucinations in large language models using semantic entropy (2024, neutral): “In Supplementary Note 2, we carefully evaluate the benefits and drawbacks of these methods for entailment prediction.”
- Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine (2023, background): “19,20 Thus, LLMs show promise to change clinical practice by allowing doctors to spend more time with their patients.”
- Utility of ChatGPT in Clinical Practice (2023, neutral): ““Hallucination” refers to the fact that the content generated by the model is not based on reality, creating a completely fabricated story or fact [24].”
- ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023, background): “, 2022), ethnics (Shen et al., 2023), human-computer collaboration”
- From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing (2023, neutral): “LLMs could be a double-edged sword [12].”
- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective (2023, background): “, 2023), ethics (Shen et al., 2023), education (Khalil and Er, 2023), and reasoning (Bang et al.”
- Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model (2023, background): “Compared to traditional supervised deep learning models, large language models can learn from data more e ciently with a two-stage training process, starting with self-supervised learning on vast amounts of unannotated data, then ne-tuned on smaller, task-speci c, annotated datasets so they can perf”
- A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges (2024, background): “LLMs are category of language models that utilizes neural networks containing billions of parameters, trained on enormous quantities of unlabeled text data using a self-supervised learning approach [5].”
- ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations (2023, background): “Additionally, the data fed into it is only up to 2021 (Kitamura, 2023; Shen et al., 2023).”
- Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References (2023, background): “Although ChatGPT is considered a valuable tool for researchers and writers, some researchers have raised concerns about its accuracy and precision, as well as its legal implications and references [3].”