Our superior use-case, which combined GPT 4 and Gemini 1.5 Pro achieved 0.82 weighted F1 score on the 83% of the data in which the two models agreed.

We propose three use-case scenarios and estimate overall weighted F1 scores ranging from 0.44 to 0.82 depending on scenario and LLM models employed. The three scenarios aim at minimal, moderate, and major human interference, respectively.

We experimented on congressional bill titles and congressional hearing descriptions. We tested six different models' performance on clasifying titles and descriptions into Compratative Agendas Project's 21 issue topic categories.

Excited to announce my recently published article with Christoffer Florczak on LLMs' multiclass classification capabilities.
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