Activation steering offers training-free and dynamic control of target behaviors in large language models, but existing methods can impair general capabilities and require domain-specific intervention designs. We introduce Capability-Preserving Neuron Steering (CANS), a unified framework that balances target-behavior c...
Yu-Chen Wu, Cheng-Yi Xie, Chen-Peng Wang et al.· 0 citations
Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms governing this selection remain unclear. We show that the choice between context and parametric knowledge is linearly represented and can be controlled along a single d...
Yibin Lou, Yi Hu, Jian-Hui Chen et al.· 0 citations
Activation steering provides a lightweight and flexible way to control large language model (LLM) behavior. However, effective steering requires more than inducing the intended behavior: it should also limit unintended changes and remain robust across inputs and training data. Existing evaluations cover these dimension...
Hao-Tian Yang, Hui-Kang Jiang, Yu-Chen Wu et al.· 1 citation
These findings show that pretrained LLMs solve propositional reasoning through a structured, layer-organized process that recurs across models, rule categories, and reasoning hops.
Dan-Qing Chen, Qi-Yao Yan, Chen-Peng Wang et al.· arXiv.org· 2 citations
Influence-guided response rewriting is introduced, which uses IF to identify intervention targets and replaces their responses with behavior-aligned or behavior-opposed supervision while keeping instructions fixed, motivating intervention-aware evaluation of TDA methods.
Yu-Zhang Luo, Chen-Peng Wang, Jian-Hui Chen et al.· 0 citations
This work introduces a diagnostic protocol using a minimal, target-label-free additive correction, showing that many apparent zero-shot reasoning deficits are expression failures masking intact internal logic, urging a narrower interpretation of benchmark evaluations.
Tail subtraction is introduced, which removes shared prompt and continuation semantics from boundary states and yields cleaner, more stable steering signals, and suggests that steering depends on representations of what the model is about to do, not merely on what has already appeared.
Jiaran Ye, Lingxu Ran, Zijun Yao et al.· arXiv.org· 2 citations
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