In everyday language use, speakers frequently utter and interpret sentences that are semantically underspecified, namely, whose content is insufficient to fully convey their message or interpret them univocally. For example, to interpret the underspecified sentence "Don't spend too much", which leaves implicit what (not) to spend, additional linguistic context or outside knowledge is needed. In this work, we propose a novel Dataset of semantically Underspecified Sentences grouped by Type (DUST) and use it to study whether pre-trained language models (LMs) correctly identify and interpret underspecified sentences. We find that newer LMs are reasonably able to identify underspecified sentences when explicitly prompted. However, interpreting them correctly is much harder for any LMs. Our experiments show that when interpreting underspecified sentences, LMs exhibit little uncertainty, contrary to what theoretical accounts of underspecification would predict. Overall, our study reveals limitations in current models' processing of sentence semantics and highlights the importance of using naturalistic data and communicative scenarios when evaluating LMs' language capabilities.

In recent months, the Internet has been set ablaze with the introduction for the public beta of ChatGPT. People across the world shared their thoughts on such an incredible development. ChatGPT relies on a subsection of machine learning, called large language models, that have already shown to be both immensely useful and potentially dangerous. I have sat down with an artificial intelligence and machine learning expert, Martynas Juravičius, from Oxylabs, a premium proxy and public web data-acquisition solution provider, and members of the company's AI advisory board, Adi Andrei and Ali Chaudhry, to discuss the importance of such models and how they may shape our future. Gary Drenik: What is a "large language model" and why are they so important going forward?

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The co-founder of a fast-growing conversational AI start-up attributes its ongoing success to the commitment to building a diverse and trusting company culture almost as much as to the quality of the technology it is offering. Andrei Papancea is CEO of conversational AI specialist NLX, which has expanded from five to 25 staff in a little over a year. The small team is geographically spread across the world, from New York to Seattle to Queensland, and Berlin, and speaks 19 different languages, including Arabic, Mandarin, Korean and Spanish. Andrei says that his mission is to combine the best of AI with the best of human support to create extraordinary, memorable self-service experiences for users by building the world's go-to platform to create human conversational AI applications. "In all the jobs I had throughout my career, I always disliked it when good people – my colleagues, my peers, and my friends – quit. They always left for one of three reasons: they weren't paid well, they didn't feel heard or respected, or they didn't have interesting and engaging work to do. In building NLX, I've done the best I can to avoid losing good people because of these three reasons," explains Andrei.

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