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This paper introduces a multilingual dataset of ethical dilemmas to probe LLMs' value preferences and proposes a task vector composition method for modular ethical alignment without retraining.
This paper proposes Vision-Free Adaptation (VFA), a framework that enhances multilingual capabilities in multimodal large language models by merging multilingual and vision-aligned task vectors without visual data, demonstrating improved performance and data efficiency.
HyperFix proposes a lightweight hypernetwork to predict nonlinear corrections for task vector merging across varying task subsets, reducing per-subset tuning costs and outperforming existing methods.
Introduces 'overthinking', a technique that amplifies reasoning weights from reasoning-distilled models to induce disclosure of hidden information in language models, demonstrating up to 10x greater secret leakage across 2B-32B models.
The paper identifies 'Load-Bearing Wall' dimensions in pre-trained models that retain task-specific knowledge not fully captured by task vectors in model merging, and proposes PACT (PreserveAnchoredCores) to preserve these cores, achieving state-of-the-art performance across benchmarks.
This paper proposes using distributional alignment between task vector-based and in-context learning inference as a criterion for designing task vectors, and introduces Linear Task Vector (LTV) that minimizes next-token probability discrepancy via closed-form linear mapping. LTV achieves 9.2% average accuracy improvement over baselines across eight benchmarks and five LLMs.