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cs.CL2024
Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs
Scott E. Friedman, Noam Benkler, Drisana Mosaphir +8
Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part…
cs.CY2024★ 1 cited
Annotator in the Loop: A Case Study of In-Depth Rater Engagement to Create a Bridging Benchmark Dataset
Sonja Schmer-Galunder, Ruta Wheelock, Scott Friedman +3
With the growing prevalence of large language models, it is increasingly common to annotate datasets for machine learning using pools of crowd raters. However, these raters often w…