Rulemapping uses expert symbolic scaffolds to constrain LLMs, raising precision on §130(1) German hate-speech classification from 0.34-0.49 to 0.80-0.86 while preserving recall of 0.82-0.89.
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Presents a distributed infrastructure for scaling skip-gram graph embeddings to 68M-vertex networks by avoiding partitioning, using dynamic size-constrained graphs, and efficient indexing for updates.
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Beyond Imperfect Alternatives with Rulemapping: A Neuro-Symbolic Case Study on Online Hate Speech
Rulemapping uses expert symbolic scaffolds to constrain LLMs, raising precision on §130(1) German hate-speech classification from 0.34-0.49 to 0.80-0.86 while preserving recall of 0.82-0.89.
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Graph Embeddings at Scale
Presents a distributed infrastructure for scaling skip-gram graph embeddings to 68M-vertex networks by avoiding partitioning, using dynamic size-constrained graphs, and efficient indexing for updates.