A 17-year-old student from Portland, Oregon, has developed a two-step training method that helps small artificial intelligence models copy the empathetic communication style of much larger systems. The method achieved higher empathy ratings in at least 90 per cent of comparison tests. Henry Xie, a senior at Westview High School in Portland, created the computer science project "Distilling Empathy From Large Language Models" for the Regeneron Science Talent Search, the leading science and mathematics competition for high school seniors in the United States. Official information published by the Society for Science confirmed that Xie was named a national finalist and received a $25,000 award for his research into ethical artificial intelligence. Xie's method tackles a key problem in current AI technology. Large language models (LLMs) with billions of parameters, such as OpenAI's ChatGPT and Google's Gemini, can produce detailed and emotionally supportive responses. However, they need large amounts of computing power, costly server systems and a constant internet connection. Small language models (SLMs), on the other hand, can run directly on devices such as smartphones, laptops and wearable technology. They do not need the same level of server support and can respond with low delay. However, these smaller models have traditionally struggled to understand emotional details and often produce mechanical or detached replies.
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