ChatGPT for (Finance) research: The Bananarama Conjecture

Dowling, Michael; Lucey, Brian M.

doi:10.1016/j.frl.2023.103662

Cited by 302 publications

(153 citation statements)

References 3 publications

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“…Similarly, Susnjak (2022) used ChatGPT in an experiment to assess its ability to engage in critical thinking rather than simply information retrieval, and the results were highly accurate and precise, as well as logically coherent. In contrast, Dowling and Lucey (2023) noted that although ChatGPT has advantages for ideas generation and data identification, it is weaker when it comes to literature synthesis and creating appropriate testing frameworks in the context of finance research.…”

Section: Literature Reviewmentioning

confidence: 99%

ChatGPT in education: Strategies for responsible implementation

Halaweh

2023

CONT ED TECHNOLOGY

232

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Since the launch of ChatGPT for public use, educators have expressed a variety of concerns about its integration into educational settings. This paper has been written to provide an in-depth examination of these issues and explore the potential use of ChatGPT in educational contexts. Specifically, it aims to (i) present an argument in favor of incorporating ChatGPT into education and (ii) provide educators with a set of strategies and techniques to ensure responsible and successful implementation of ChatGPT in teaching or research. By doing so, this paper aims to promote a more informed discussion around the use of ChatGPT in education.

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Section: Literature Reviewmentioning

confidence: 99%

ChatGPT in education: Strategies for responsible implementation

Halaweh

2023

CONT ED TECHNOLOGY

232

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“…On the positive side, a literature review concluded that scientific publishing would be greatly facilitated in the future, requiring less human work while allowing researchers to focus more on their scientific tasks (Aydın & Karaarslan, 2022). Another recent work supported this conclusion suggesting that ChatGPT could generate viable research questions, literature overviews, and test ideas (Dowling & Lucey, 2023). Due to human feedback, areas of consistent knowledge and major topics could generate more accurate answers than narrow specific topics, especially if they are accompanied by numerous controversial information in various databases.…”

Section: Introductionmentioning

confidence: 87%

ChatGPT a breakthrough in science and education: Can it fail a test?

Szabó¹

2023

Preprint

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Released less than three months ago, ChatGPT became the center of attention of scholars around the world. This artificial intelligence (AI) language model has over 100 million subscribers worldwide and generates many discussions concerning its accuracy, advantages, and threats to science and education. Its accuracy in law, linguistics, mathematics, and medicine has already been evaluated. Most results suggest that ChatGPT could generate a passing grade in these domains. Its performance in combined subjects or social sciences has yet to be tested. The large amount of information in this general area may yield more accurate performance. Still, specific subjects in the field, with controversial research findings, can lead to significant errors, which teachers and researchers could quickly spot. In this study, ChatGPT was tested on its accuracy on exercise addiction, a subject in sports sciences and psychology associated with more than 1,000 publications. ChatGPT gave several correct answers to 20 questions but failed the test with 45%. Its performance was like in other already tested subjects. However, when prompted to write a general introductory editorial on AI’s role in sports, ChatGPT performed well. Plagiarism detectors could not identify the AI-originated text, but AI detectors did. Therefore, it can be concluded that the system does a relatively good job on general issues but needs further development in more specific areas. Students and scholars cannot rely on ChatGPT to do their job. Still, future versions could yield dilemmas of originality since the system does not provide information for its source(s) of information.

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“…Beyond computer science, ChatGPT can be readily applied to medical report generation and comprehension [58], [59], education [60], [61], [62], rigorous math research [63] and finance [64]. Overall, ChatGPT is a versatile tool that promotes general AI usage.…”

Section: Chatgpt: Present and Futurementioning

confidence: 99%

AugGPT: Leveraging ChatGPT for Text Data Augmentation

Dai¹,

Liu²,

Liao³

et al. 2023

Preprint

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Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely-used strategy to mitigate such challenges is to perform data augmentation on the training data to better capture the data invariance and increase the sample size. However, current text data augmentation methods either can not ensure the correct labeling of the generated data (lacking faithfulness) or can not ensure sufficient diversity in the generated data (lacking completeness), or both. Inspired by the recent success of large language models, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, we propose a text data augmentation approach based on ChatGPT (named ChatAug). ChatGPT is trained on data with unparalleled linguistic richness and employs a reinforcement training process with large-scale human feedback, which endows the model with affinity to the naturalness of human language. Our text data augmentation approach ChatAug rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed ChatAug approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.

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ChatGPT for (Finance) research: The Bananarama Conjecture

Cited by 302 publications

References 3 publications

ChatGPT in education: Strategies for responsible implementation

ChatGPT in education: Strategies for responsible implementation

ChatGPT a breakthrough in science and education: Can it fail a test?

AugGPT: Leveraging ChatGPT for Text Data Augmentation

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