Email: ndeas AT cs DOT columbia DOT edu
Office: Schapiro CEPSR 7LW1
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Research Interests
Linguistic Biases in LLMs
Much of the text found online used for pre-training language models reflects General American English, leading to models that are less capable of understanding other dialects and language varieties of English such as African American Language. This research direction seeks to evaluate and mitigate linguistic biases to help create models capable of understanding a wider diversity of language.
Modeling Social Psychology
As language models continue to be applied in psychological research and mental health settings, they must be capable of interpreting linguistic expressions of emotion or mental health. This research direction draws on theories and findings in social psychology and psycholinguistics to embue models with better understanding of human interpersonal behavior.
Political Perspectives and Polarization
With increased interaction on social media and a diverse array of media sources, it is important to ensure that language models fairly learn from and aid productive public discourse. This research direction aims to evaluate the political alignment of language models, ensure fair representation of perspectives on issues, and build models capable of understanding nuances of political speech and rhetoric.
Publications
[NLP: NLP and Computational Linguistics , P: Psychology, PS: Political Science]
[NLP] Evaluation of African American Language Bias in Natural Language Generation
Nicholas Deas, Jessi Grieser, Shana Kleiner, Desmond Patton, Elsbeth Turcan, Kathleen McKeown[NLP,P] I just want to matter: Examining the role of anti-mattering in online suicide support communities using natural language processing
Nicholas Deas, Robin Kowalski, Sophie Finnell, Emily Radovic, Hailey Carroll, Chelsea Robbins, Andrew Cook, Kenzie Hurley, Natalie Cote, Kelly Evans, Isabella Lorenzo, Kelly Kiser, Gabriela Mochizuki, Meredith Mock, and Lyndsey Brewer.[P] Protection Motivation Theory and intentions to receive the COVID-19 vaccine.
Robin M. Kowalski, Nicholas Deas, Noah Britt, Emily Richardson, Sophie Finnell, Kelly Evans, Hailey Carroll, Andrew Cook, Emily Radovic, Tanner Huyck, Isabella Parise, Chelsea Robbins, Hannah Chitty, and Sophie Catanzaro.[PS] Partisan Differences in Politicians’ Rhetoric about COVID-19, and Why These Messages Spread Online.
Nicholas Deas, Cierra Oliveira, Riley Stotzky, and Leah Terry[NLP] Systematic evaluation and enhancement of speech recognition in operational medical environments.
Snigdhaswin Kar, Prabodh Mishra, Ju Lin, Min-Jae Woo, Nicholas Deas, Caleb Linduff, Sufeng Niu, Yuzhe Yang, Jerome McClendon, D. Hudson Smith, Melissa C. Smith, Ronald W. Gimbel, and Kuang-Ching Wang[NLP] Complete and resilient documentation for operational medical environments leveraging mobile hands-free technology in a systems approach: Experimental study
Minjae Woo, Prabodh Mishra, Ju Lin, Snigdhaswin Kar, Nicholas Deas, Caleb Linduff, Sufeng Niu, Yuzhe Yang, Jerome McClendon, D. Hudson Smith, Stephen L. Shelton, Christopher E. Gainey, William C. Gerard, Melissa C. Smith, Sarah F. Griffin, Ronald W. Gimbel, and Kuang-Ching Wang[NLP, PS] Using Natural Language Processing to Automate Detection of Targeted Attacks in Political Tweets.
Nicholas Deas, Jacob Sargent, and Spencer Shellnut[NLP, PS] How Characteristics of Members of the House of Representatives and the Political Environment Affect the Use of Political Attacks on Twitter.
Jacob Sargent, Spencer Shellnut, and Nicholas DeasMPSA Best Undergraduate Research Award