They found that "according to" prompts successfully directed language models to ground their responses against previously observed text rather than hallucinating false answers, the models are more likely to directly quote the requested source-just like a journalist would, the team says. Inspired by a phrase commonly used in journalism, the researchers conducted a study on the impact of incorporating the words "according to" in LLM queries. Marone and a team of researchers that included doctoral candidates Orion Weller and Nathaniel Weir and advisers Benjamin Van Durme, an associate professor of computer science and a member of the Center for Language and Speech Processing Dawn Lawrie, a senior research scientist at the Human Language Technology Center of Excellence and Daniel Khashabi, an assistant professor of computer science and also a member of CLSP, developed a method to reduce the likelihood that LLMs hallucinate. "Imagine using your phone's autocomplete function to finish the sentence 'My favorite restaurant is.' You'll probably wind up with some reasonable-looking text that's not necessarily accurate," explains Marc Marone, a third-year doctoral candidate in the Whiting School of Engineering's Department of Computer Science.
Last week, Johns Hopkins AI pioneer Rama Chellappa hosted his first Reddit AMA, answering any and all questions about machine learning
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