4 papers · 1 filter
When Every Choice Is Correct: A Causal Study of Numerical Preference in Language Models
Hongnian Huang, Hongliang Liu
When a language model chooses a number between 1 and 30, every integer in the range is valid, yet the model can favor particular answers. We use numerical choice to study how prefe…
Breaking Safety at the Token Boundary: How BPE Tokenization Creates Exploitable Gaps in LLM Alignment
Tung-Ling Li, Hongliang Liu, Yuhao Wu
Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural mechanism: BPE tokenization f…
Leverage Is Not Reach: A Control-Window Law for Single-Neuron Steering in Language Models
Hongliang Liu
Aligned language models gate behaviors such as refusal and language routing through sparse feed forward neurons, yet no theory predicts when a single neuron intervention controls a…
Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs
Hongliang Liu, Tung-Ling Li, Yuhao Wu
Perturbation probing generates task-specific causal hypotheses for FFN neurons in large language models using two forward passes per prompt and no backpropagation, followed by a on…