pllum-b5f58a80·1 events·first seen Aliases: PLLuM
Researchers probe activations at the final prompt token across twelve instruction-tuned models (Bielik, PLLuM, Gemma-4, Qwen3 families) to assess whether LLMs encode graded familiarity with named entities before generating answers. Using a new dataset of 1,440 Polish entities across popularity deciles plus fabricated controls, they find familiarity probes reliably separate real from fabricated entities and track popularity in Polish-adapted models. A key finding is that a single one-dimensional familiarity direction injected at one layer in Gemma-4-12B can steer refusal rates monotonically from 0.24 to 1.00 (or 0.73 to 0.00), revealing a separation between representational familiarity and the policy that converts it into abstention behavior.