The December 2022 paper in the cancer journal Oncoscience appeared to be a conventional discussion of the pros and cons of treating patients with the drug rapamycin[1].But the article was written using artificial inte...The December 2022 paper in the cancer journal Oncoscience appeared to be a conventional discussion of the pros and cons of treating patients with the drug rapamycin[1].But the article was written using artificial intelligence(AI)and listed the AI chatbot ChatGPT as its lead author.The large language model(LLM)built by OpenAI(San Francisco,CA,USA)had made its sensational public debut less than a month before[2],and the paper was one of the first scientific publications to credit it as an author[3].展开更多
The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage A...The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage AI customer service chatbots for societal welfare.Across two scenario studies and one lab experiment,this research investigates the impact of AI chatbots’communication styles on consumers’subsequent prosocial intentions irrelevant to the AI-human interaction contents.The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented(vs.task-oriented)AI chatbots.The findings reveal the chain-mediating roles of social presence and empathy.Moreover,the current research investigates the boundary effect of consumers’goal focus(process focus vs.outcome focus),and shows that AI chatbots’communication styles have stronger impact on prosocial intentions for customers with outcome focus.These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility(CSR)strategy.展开更多
We have read with great interest the article“A new ChatGPT-empowered,easy-to-use machine learning paradigm for environmental science”published in volume 3,Eco-Environment&Health[1].An and colleagues[1]proposed t...We have read with great interest the article“A new ChatGPT-empowered,easy-to-use machine learning paradigm for environmental science”published in volume 3,Eco-Environment&Health[1].An and colleagues[1]proposed to investigate the multifaceted sentimental complexities of usage and adversities of ChatGPT,a generative AI chatbot developed by Open artificial intelligence(OpenAI),in various medical contexts in the digital age of medicine.The authors introduced a new research paradigm combining ChatGPT and machine learning(ML)to facilitate the application of ML in environmental science.This paradigm is proposed to attenuate the complexity of using ML models for ecological data analysis,making it more accessible for researchers with limited AI experience.展开更多
Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developi...Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically unrevealed.Since datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune processes.The paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art LLMs.Secondly,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and concerns.This study then critically figures out the problems and research trends that exist in current LLM datasets.The study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data development.To the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat LLMs.The survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.展开更多
文摘The December 2022 paper in the cancer journal Oncoscience appeared to be a conventional discussion of the pros and cons of treating patients with the drug rapamycin[1].But the article was written using artificial intelligence(AI)and listed the AI chatbot ChatGPT as its lead author.The large language model(LLM)built by OpenAI(San Francisco,CA,USA)had made its sensational public debut less than a month before[2],and the paper was one of the first scientific publications to credit it as an author[3].
基金supported in part by the National Natural Science Foundation of China(NSFC),under Grants Nos.72301034 and 72272016Fundamental Research Funds for the Central Universities under Grant No.2025ZZ048.
文摘The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage AI customer service chatbots for societal welfare.Across two scenario studies and one lab experiment,this research investigates the impact of AI chatbots’communication styles on consumers’subsequent prosocial intentions irrelevant to the AI-human interaction contents.The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented(vs.task-oriented)AI chatbots.The findings reveal the chain-mediating roles of social presence and empathy.Moreover,the current research investigates the boundary effect of consumers’goal focus(process focus vs.outcome focus),and shows that AI chatbots’communication styles have stronger impact on prosocial intentions for customers with outcome focus.These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility(CSR)strategy.
文摘We have read with great interest the article“A new ChatGPT-empowered,easy-to-use machine learning paradigm for environmental science”published in volume 3,Eco-Environment&Health[1].An and colleagues[1]proposed to investigate the multifaceted sentimental complexities of usage and adversities of ChatGPT,a generative AI chatbot developed by Open artificial intelligence(OpenAI),in various medical contexts in the digital age of medicine.The authors introduced a new research paradigm combining ChatGPT and machine learning(ML)to facilitate the application of ML in environmental science.This paradigm is proposed to attenuate the complexity of using ML models for ecological data analysis,making it more accessible for researchers with limited AI experience.
文摘Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically unrevealed.Since datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune processes.The paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art LLMs.Secondly,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and concerns.This study then critically figures out the problems and research trends that exist in current LLM datasets.The study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data development.To the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat LLMs.The survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.
