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Challenges and Progress towards Socially Aware NLP for Positive Impact
Abstract: Despite the remarkable performance of NLP these days, current systems often ignore the social part of language, e.g., who says it, or what goals, and with what social implications, all of which severely limits the functionality of these applications and the growth of the field. This talk will discuss some of our recent efforts towards socially aware NLP via two studies. The first part looks at how large language models work in the context of social understanding, and how human-AI collaboration can reduce costs and improve the efficiency of social science research. The second part introduces CARE, an interactive AI agent that supports counselors through LLM-empowered feedback and deliberate practices. I conclude by discussing the challenges and hidden risks of building socially aware NLP systems for positive impact.