Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

By Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Haoyang He, Antong Li, Mengshen He, Zheng Liu and 7 more. 2023

263 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics

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In Artificial Intelligence

Citations in the corpus

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models (2023, methodology): “Due to the lack of emotion-labeled audiobook text datasets, we used GPT-4 to generate 500 utterances across five emotions for this task [64].”
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions (2024, background): “…success of these foundation models has spawned numerous derivative works and applications spanning different industries, which have become an essential component of many AI system architectures, and their continued development promises to drive further advances in language and vision tasks [34, 8].”
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (2023, unknown): “, 2022) and is cited as one of the key drivers of success in modern conversational language models like ChatGPT and Bard (Liu et al., 2023; Manyika, 2023).”
Generative AI at Work (2023, unknown): “Finally, general-purpose LLMs can be further “fine-tuned” to generate output that matches the priorities of any specific setting (Ouyang et al., 2022; Liu et al., 2023).”
Gender bias and stereotypes in Large Language Models (2023, methodology): “even excel at tests such as the SAT, the LSAT, medical school examinations, and IQ tests (see [57] for a comprehensive summary).”
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (2023, background): “Recent works have also explored related techniques for generating rewards from LLMs (Roit et al., 2023; Kwon et al., 2022; Yang et al., 2023).”
Mobile Game Evaluation Method Based on Data Mining of Affective Time Series (2025, unknown): “Finally, introducing the concept of specific game modules, which allows us to interpret the results of the analysis in a given context of the mobile game.”