Can ChatGPT Write a Literature Review (Related Work) Section of a Scientific Paper?

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In this video we analyze a Literature Review section written by ChatGPT.
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ChatGPT Related Work Section:
Energy conservation has become a major concern in recent years due to the increasing demand for energy and the impact of energy consumption on the environment. Various approaches have been proposed to reduce energy consumption in residential buildings. One such approach is the use of smart meters and real-time feedback to provide users with information about their energy usage and encourage them to adopt energy-saving behaviors [1]. However, research has shown that these approaches are not effective in changing user behavior in the long term [2].
Another approach is to use financial incentives to motivate users to reduce their energy consumption. The use of economic incentives such as time-of-use pricing and demand response programs has been shown to be effective in reducing peak energy demand and shifting energy consumption to off-peak periods [3]. However, these approaches do not consider the user's preferences and may not be suitable for all users.
Reverse auction theory has been used in the context of energy markets to allocate energy resources efficiently [4]. The idea is to allow buyers to bid for energy resources, and the seller chooses the bids that maximize their profit. The same concept can be applied to incentivize energy conservation in residential buildings. By allowing users to bid for energy savings, they are incentivized to adopt energy-saving behaviors that result in actual energy savings.
Machine learning has been used in the context of energy conservation to develop predictive models that can forecast energy consumption and optimize energy usage [5]. These models can be used to provide personalized recommendations to users and improve the effectiveness of energy conservation programs.
The proposed framework combines the use of reverse auction theory and machine learning to develop a human-centered approach to energy conservation. By allowing users to bid for energy savings, the framework incentivizes energy-saving behaviors that are aligned with user preferences. The use of machine learning enables personalized recommendations that are tailored to each user's energy consumption patterns.
References:
[1] Darby, S. (2006). The effectiveness of feedback on energy consumption: A review for DEFRA of the literature on metering, billing and direct displays. Environmental Change Institute, University of Oxford.
[2] Faruqui, A., Sergici, S., & Sharif, A. (2010). The impact of informational feedback on energy consumption-A survey of the experimental evidence. Energy, 35(4), 1598-1608.
[3] Li, H., Xu, Z., Sun, W., Wang, L., & Zhang, J. (2015). A review of demand response in smart grid. Renewable and Sustainable Energy Reviews, 44, 569-582.
[4] Kwon, H. J., & Chae, Y. M. (2017). Energy auction mechanism for energy management in a smart grid environment. Journal of Ambient Intelligence and Humanized Computing, 8(2), 247-256.
[5] Hong, T., Yan, D., & D'Oca, S. (2017). Machine learning algorithms for predicting building energy consumption: A review and comparative analysis. Applied Energy, 194, 135-162.
Bio: Dr. Silvestri is currently an Associate Professor in the Department of Computer Science of the University of Kentucky. Before joining UK, Dr. Silvestri was a Post-Doctoral Research Associate in the Department of Computer Science and Engineering at Pennsylvania State University. He received his Ph.D. in Computer Science in 2010 from the Department of Computer Science of the Sapienza University of Rome, Italy. Dr. Silvestri's research is funded by several national and international agencies such as NIFA, NATO and the NSF, and he received the NSF CAREER award in 2020. He published more than 80 papers in international journals and conferences including IEEE Transactions on Mobile Computing, IEEE Transactions on Smart Grids, ACM Transactions on Sensor Networks, IEEE INFOCOM, and IEEE ICDCS. He served in the organizing committee of several international conferences including as General Co-Chair of IEEE ICNP, Technical Program Co-Chair of IEEE SECON, IEEE SmartComp, and IEEE DCOSS. He also served in the Technical Program Committee of more than 100 conferences, including IEEE INFOCOM, IEEE ICNP, IEEE SECON and IEEE GLOBECOM.
Web: silvestri.engr.uky.edu/
Email: [e-mail]
Google Scholar: scholar.google.com/citations?...
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[@user-wr2qn8jk7w]

Thank you for the video. I hope it is an eye opener for many of us. I also used it to obtain some references / citations for a topic that I was not very familiar with and I just wanted to read some relevant papers. In fact none of the 5 citation that ChatGPT gave me actually existed.

[@csmentor5482]

Thank, Anastasius. I am concerned about several potential side effects of using this technology. Some that came to mind are:
- Students do not read as many papers and just rely on AI
- Students do not develop critical thinking which is fundamental for advancing science
- Non-existing citations might fell into the cracks and end up in published papers. Reviewers will not check citation by citation and advisors will not check their students' paper that carefully.
- There will be (and there is) a general sense of trust for a technology that has a natural tendency to create information that is potential false.

[@muhammadshahidiqbal7708]

Same happened to me.

[@csmentor5482]

Please let me know your opinion and experience in using AI assisted technology to write scientific papers.
Thank you for watching!

[@theBatchNorm]

You are using it in a wrong way, and expected a right outcome!

[@csmentor5482]

How would you have suggested to use it in this case?
My objective is to show that simple uses like this one, which many may be encouraged to do, may lead to results that are incorrect, and difficult to detect.

[@funnyvideosandskits]

Shortly, no.

[@csmentor5482]

Yes, not yet at least.