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Experiments show that the proposed Preference Vector framework improves helpfulness without excessive conservatism, allows smooth control over preference trade-offs, and supports scalable multi-preference alignment.
This work proposes a geometry-aware composite metric that jointly leverages Euclidean distance and cosine similarity to capture token diversity from complementary perspectives and uses these two metrics to estimate the redundancy level of each attention head, allowing adaptive weighting between diversity and importance...
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