#aBitOfCCS on Performance vs. Sustainability in Text Analysis with Sean Palicki hosted by Jana Bernhard-Harrer
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#aBitOfCCS on Performance vs. Sustainability in Text Analysis with Sean Palicki hosted by Jana Bernhard-Harrer
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Tune in to the #aBitOfCCS Podcast as we dig into the growing tension between performance and sustainability in computational text analysis. Sean Palicki, a researcher at TUM, joins us to discuss his recent paper Don’t Look Up: Evaluating the Tradeoff between Performance and Sustainability of LLMs for Text Analysis.
In this episode, we explore how large language models (LLMs) compare to lighter methods such as dictionaries and task-specific classifiers when applied to sentiment analysis, classification, and named entity recognition in political texts. We talk about the environmental costs of relying on large models, why bigger doesn’t always mean better for text analysis, and how introducing a CO₂-adjusted F1 score can help balance accuracy with sustainability.
The conversation highlights a “right-fit” approach to model selection—choosing tools that are not only effective but also environmentally responsible.
Reach out to Sean at sean.palicki@tum.de and find his website here: https://sean.web-of-us.com/
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