There was a time when certain shops would resemble nightclubs at about midnight: a long queue of excitable people, some of them perhaps too young to be out that late, discussing the excitement that awaits inside. The sight of throngs of gamers looking to get their hands on the latest hardware when the clock strikes 12 is growing increasingly rare. But if you happen to walk by a Smyths toy shop at midnight on 4 June, you may encounter a blast from the past: excitable people, most in their teens or 20s, possibly discussing Mario Kart. They will be waiting to buy the Nintendo Switch 2, the first major games console launch since 2020 and potentially the biggest of all time. What's particularly notable about this launch isn't the queues but just how few there will be.

The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. Machine Translation (MT) has been shown to benefit from in-context examples, in particular when they are semantically similar to the sentence to translate. In this paper, we propose a new LLM-based translation paradigm, compositional translation, to replace naive few-shot MT with similarity-based demonstrations. An LLM is used to decompose a sentence into simpler phrases, and then to translate each phrase with the help of retrieved demonstrations. Finally, the LLM is prompted to translate the initial sentence with the help of the self-generated phrase-translation pairs. Our intuition is that this approach should improve translation because these shorter phrases should be intrinsically easier to translate and easier to match with relevant examples. This is especially beneficial in low-resource scenarios, and more generally whenever the selection pool is small or out of domain. We show that compositional translation boosts LLM translation performance on a wide range of popular MT benchmarks, including FLORES 200, NTREX 128 and TICO-19. Code and outputs are available at https://github.com/ArmelRandy/compositional-translation

In 2002, Target hired statistician Andrew Pole. His job was to use predictive analytics -- a form of statistics that makes predictions by observing data trends -- to help the retail giant market certain products to certain groups of people. Along those lines, Pole's first task was to identify pregnant women -- specifically women in their second trimester. As Target's marketing team explained to him, new parents are extremely valuable customers whose brand loyalty tends to change when they have kids because they purchase things they probably weren't purchasing before -- like diapers, formula, baby clothes, etc. New parents also tend to be physically exhausted and therefore more prone to do all of their shopping at one place.