The aerospace industry can benefit from conversational agents that provide efficient solutions for safety-of-life scenarios. This industry is characterized by products and systems that require years of engineering to achieve optimal performance within complex environments. With recent advances in retrieval and language models, conversational agents can be developed to enhance the system’s question-answering capabilities. However, evaluating the added-value of such systems in the context of industrial applications, such as pilots in a cockpit, can be challenging. This article presents the design, implementation, and user evaluation of a conversational agent called Smart Librarian, which is tailored to the aerospace domain’s specific requirements to support pilots in their tasks. Our results based on a controlled user experiment with flight school students indicate that the user’s perception of the usefulness of the system in completing the search task is a good predictor of both task score and time spent. The system’s responses to the relevance of the topic is also a good predictor of task score. The perceived difficulty of the search task and its interaction with the relevance of the system’s responses to the topic also play a key role in search performance. The mixed-effects models constructed in this study had large effect sizes, demonstrating participants’ ability to assess their performance accurately. Nevertheless, user satisfaction with the system’s responses may not be a reliable predictor of user search performance. Implications for the design of conversational agents based on the domain’s specific requirements are discussed.

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

Mission planning for Earth Observation Satellite operators typically implies dynamically altering how requests from different customers are prioritised in order to meet expected deadlines. A request corresponds to a given area of interest to capture on Earth. This exercise is challenging for different reasons. First, satellites are limited by maneuvers and power consumption constraints resulting in a limited surface that can be covered at each orbit. Consequently, many requests are in competition with each other and so all of them cannot be treated at each orbit. When a request priority is boosted, it may incidentally penalise surrounding requests. Second, there are several uncertain factors such as weather (in particular cloud cover) and future incoming requests that can impact the completion progress of the requests. With order books of increasing size and the planned operations of a growing number of satellites in a close future, there is a clear need for a decision support method.