Associate Professor Smaranda Muresan

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing (NLP) and its applications across all domains. Models such as ChatGPT seem to possess human-like abilities — reasoning about problems, passing bar exams, writing stories. But do they? In trying to answer this question, I will discuss three main desiderata for building human-centric NLP systems: knowledge-aware models, human-AI collaboration frameworks, and theoretically-grounded evaluation protocols. In this talk, I will use argumentation and creativity as two case studies. I will cover knowledge-aware models for implicit premise generation, human-AI collaboration framework for high-quality datasets creation (e.g., visual metaphors) and helping human solve tasks (e.g., writing short stories), and last but not least a novel evaluation protocol for assessing the creative capabilities of LLMs in both producing as well as assessing creative text.

Smaranda Muresan is a Research Scientist at the Data Science Institute at Columbia University, a Visiting Associate Professor at Barnard College and an Amazon Scholar. Her research focuses on human-centric Natural Language Processing for social good and responsible computing. She develops theory-guided and knowledge-aware computational models for understanding and generating language in context (e.g., visual, social, multilingual, multicultural) with applications to computational social science, education, and public health. Research topics that she worked on over the years include: argument mining and generation, fact-checking and misinformation detection, figurative language understanding and generation (e.g., sarcasm, metaphor, idioms), and multilingual language processing for low-resource and endangered languages. Recently, her research interests include explainable models and human-AI collaboration frameworks for high-quality datasets creation. She received best papers awards at SIGDIAL 2017 and ACL 2018 (short paper). She served as a board member for the North American Chapter of the Association for Computational Linguistics

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