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The paper proposes a reference-based hidden-state auditing method to detect bias shifts in LLMs with minimal compute, showing high correlation with output-level bias changes across benchmarks.
This paper proposes improvements to Relative Representations by learning robust semantic anchors and using a geometry-aware similarity metric, enabling nearly lossless information transfer and stable zero-shot communication between independently trained models of varying architectures.