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The curious case of the disappearing Lamborghinis

MIT Technology Review

A new wave of theft is rocking the luxury car industry--mixing high-tech with old-school chop-shop techniques to snag vehicles while they're in transport. When Sam Zahr first saw the gray Rolls-Royce Dawn convertible with orange interior and orange roof, he knew he'd found a perfect addition to his fleet. "It was very appealing to our clientele," he told me. As the director of operations at Dream Luxury Rental, he outfits customers in the Detroit area looking to ride in style to a wedding, a graduation, or any other event with high-end vehicles--Rolls-Royces, Lamborghinis, Bentleys, Mercedes G-Wagons, and more. But before he could rent out the Rolls, Zahr needed to get the car to Detroit from Miami, where he bought it from a used-car dealer. His team posted the convertible on Central Dispatch, an online marketplace that's popular among car dealers, manufacturers, and owners who want to arrange vehicle shipments. It's not too complicated, at least in theory: A typical listing includes the type of vehicle, zip codes of the origin and destination, dates for pickup and delivery, and the fee. Anyone with a Central Dispatch account can see the job, and an individual carrier or transport broker who wants it can call the number on the listing. Zahr's team got a call from a transport company that wanted the job. They agreed on the price and scheduled pickup for January 17, 2025.



Datasheet Y ubo Ma

Neural Information Processing Systems

Q1: F or what purpose was the dataset created? As stated in Section 1, most previous datasets on DU focus on single-page DU. Our benchmark is constructed to bridge such a gap. Q2: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q3: What support was needed to make this dataset? Q1: What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?



A Additional Results

Neural Information Processing Systems

The acronym dataset is a QA task that requires models to decode financial acronyms. The FinMA7B-full model achieved the highest ROUGE-1 score of 0.12 and the B.1 Why was the datasheet created? B.2 Has the dataset been used already? If so, where are the results so others can compare (e.g., links to published papers)? Y es, the dataset has already been used. It was employed in the FinLLM Share Task during the FinNLP-AgentScen Workshop at IJCAI 2024, known as the FinLLM Challenge.