Chatgpt's Scientific Writings: A Case Study on Traffic Safety

Kutela, Boniphace; Msechu, Kelvin; Das, Subasish; Kidando, Emmanuel

doi:10.2139/ssrn.4329120

Cited by 11 publications

(7 citation statements)

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“…Recently, advanced language models have been adopted in several studies to solve transportation science problems ( 25 – 28 ). This research aims to use a small data set to obtain a good prediction for a crash type classification problem using an advanced language model.…”

Section: Discussionmentioning

confidence: 99%

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Weng

Das

Paal

2023

Transportation Research Record: Journal of the Transportation R

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Pedestrian deaths account for 23% of all road traffic fatalities worldwide. After declining for three decades, pedestrian fatalities in the United States have been increasing with 6,941 fatalities in 2020, the highest number for more than two decades, impeding progress toward a zero-deaths transportation system. The Pedestrian and Bicycle Crash Analysis Tool (PBCAT) was developed to describe the pre-crash actions of the parties involved to better define the sequence of events and precipitating actions that lead to crashes involving motor vehicles and pedestrians or cyclists. Undoubtedly, police crash data influence decision-making processes in the transportation agencies. Using crash data from three major cities in Texas (during the period from 2018 to 2020), this study assessed the data quality of police-reported crash narratives on pedestrian-involved traffic crashes. As the pedestrian crash typing involves many categories, conventional machine-learning algorithms will not be sufficient in solving the classification problem from narrative texts. This study used few-shot learning (FSL), an advanced machine learning, to solve this issue. Using the pre-knowledge obtained from five different crash types and a few labeled data points of three unseen new crash types, the proposed model achieved roughly 40% overall accuracy. Also, four different configurations of crash types were formed and tested which indicates that the model is robust.

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Section: Discussionmentioning

confidence: 99%

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Weng

Das

Paal

2023

Transportation Research Record: Journal of the Transportation R

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“…[33,34,35,36,27]). However, such tasks as humor identification and generation [37], machine translation [38], sentiment recognition [39], paraphrasing [40], and other text generation subtasks were also analyzed [41,42,43]. In most cases, the evaluation was conducted manually.…”

Section: Even Bigger Modelmentioning

confidence: 99%

Chatgpt: Jack of All Trades, Master of None

Kocoń

Cichecki

Kaszyca

et al. 2023

Preprint

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“…The results revealed a substantial difference between ChatGPTgenerated and human-generated introductions, with higher accuracy of supervised text classifiers, but inconsistent results in keyword frequency and cluster analysis. The work contributes to a broader understanding of the use of advanced language models in scientific writing [63]. Jiao et al evaluate ChatGPT's machine translation capabilities, such as translation quick, multilingual translation, and robustness in their paper.…”

Section: "Yes I Can Write a Scientific Article On A Given Topic As A ...mentioning

confidence: 99%

Is ChatGPT Leading Generative AI? What is Beyond Expectations?

AYDIN,

KARAARSLAN

2023

Academic Platform Journal of Engineering and Smart Systems

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Generative AI has the potential to change the way we do things. The chatbot is one of the most popular implementation areas. Even though companies like Google and Meta had chatbots, ChatGPT became popular as it was made publicly available. Although ChatGPT is still in the early stages of its development, it attracted the attention of people and capital groups. It has taken the public interest; people from different fields, ages, and education levels started using ChatGPT. There have been many trials with ChatGPT. It is possible to see a lot of news and shares on the Internet. The study aims to shed light on what is happening in the literature and get an insight into the user expectations of ChatGPT and Generative AI. We also give information about the competitors of ChatGPT, such as Google’s Bard AI, Claude, Meta’s Wit.ai and Tencent’s HunyuanAide. We describe technical and structural fundamentals and try to shed light on who will win the race. We also shared information about the GPT4 version of OpenAI's ChatGPT. We share the early stage due diligence and current situation analysis for all these points. We examine preprint papers and published articles. We also included striking posts on the LinkedIn platform and a compilation of various blogs and news. We also made use of ChatGPT in editing the content of these resources of this study. We can get an insight into the people's interests through their questions submitted to ChatGPT. We can also understand the capabilities of GPT3, GPT4 and also predict further enhancements.

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Chatgpt's Scientific Writings: A Case Study on Traffic Safety

Cited by 11 publications

References 0 publications

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Applying Few-Shot Learning in Classifying Pedestrian Crash Typing

Chatgpt: Jack of All Trades, Master of None

Is ChatGPT Leading Generative AI? What is Beyond Expectations?

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