SPLoRA prunes LoRA layers with the largest projection residual to a safety-aligned direction, reducing attack success rates while roughly preserving utility on several LLM benchmarks.
Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings
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abstract
Cosine similarity is widely used to measure the similarity between two embeddings, while interpretations based on angle and correlation coefficient are common. In this study, we focus on the interpretable axes of embeddings transformed by Independent Component Analysis (ICA), and propose a novel interpretation of cosine similarity as the sum of semantic similarities over axes. The normalized ICA-transformed embeddings exhibit sparsity, enhancing the interpretability of each axis, and the semantic similarity defined by the product of the components represents the shared meaning between the two embeddings along each axis. The effectiveness of this approach is demonstrated through intuitive numerical examples and thorough numerical experiments. By deriving the probability distributions that govern each component and the product of components, we propose a method for selecting statistically significant axes.
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2025 1verdicts
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Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
SPLoRA prunes LoRA layers with the largest projection residual to a safety-aligned direction, reducing attack success rates while roughly preserving utility on several LLM benchmarks.