Google DeepMind's new paper: CALLIOPE builds topic trees using keywords, outperforming embedding vectors
A new paper from Google DeepMind proposes CALLIOPE: it has an LLM tag each document with a small number of keywords plus noise, keeps only high-frequency keywords, and then has the LLM propose topics and classify documents accordingly, thereby generating clearer topic maps under strict privacy constraints. Under the strictest privacy setting, human raters preferred CALLIOPE over the embedding-vector-based URANIA on all 3 datasets, with the highest margin being 76% versus 18%. This method consumes 2 to 5 times as many tokens as the latter.
A new paper from Google DeepMind proposes CALLIOPE: it has an LLM tag each document with a small number of keywords plus noise, keeps only high-frequency keywords, and then has the LLM propose topics and classify documents accordingly, thereby generating clearer topic maps under strict privacy constraints. Under the strictest privacy setting, human raters preferred CALLIOPE over the embedding-vector-based URANIA on all 3 datasets, with the highest margin being 76% versus 18%. This method consume
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