Generative AI and deceptive news consumption

Sandrini, Luca; Somogyi, Robert

doi:10.1016/j.econlet.2023.111317

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…In considering the opportunity/risk profile for GenAI models, GenAI may be viewed as a DI [26,27] with effects similar to those discussed in research addressing TD [28][29][30], while GenAI offers many opportunities, there are also concerns around the use of GenAI models including cyberchrime, fake news, and deepfakes [31], all of which can be used to deceive or manipulate people. Notwithstanding the ethical and practical challenges, the uptake of GenAI has demonstrated its potential.…”

Section: Generative Artificial Intelligence and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the `Phoenix’ multilingual chatbot model by achieving a 92% performance compared to `ChatGPT’ for the English benchmark.

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Section: Generative Artificial Intelligence and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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Large language models reduce public knowledge sharing on online Q&A platforms

del Rio-Chanona,

Laurentsyeva,

Wachs

2024

PNAS Nexus

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Large language models (LLMs) are a potential substitute for human-generated data and knowledge resources. This substitution, however, can present a significant problem for the training data needed to develop future models if it leads to a reduction of human-generated content. In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM. To test whether this reduction in activity is specific to the introduction of this LLM, we use counterfactuals involving similar human-generated knowledge resources that should not be affected by the introduction of ChatGPT to such extent. Within 6 months of ChatGPT’s release, activity on Stack Overflow decreased by 25% relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable. We interpret this estimate as a lower bound of the true impact of ChatGPT on Stack Overflow. The decline is larger for posts related to the most widely used programming languages. We find no significant change in post quality, measured by peer feedback, and observe similar decreases in content creation by more and less experienced users alike. Thus, LLMs are not only displacing duplicate, low-quality, or beginner-level content. Our findings suggest that the rapid adoption of LLMs reduces the production of public data needed to train them, with significant consequences.

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