7 papers
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
Dongxin Guo, Jikun Wu, Siu Ming Yiu
Large Language Models exhibit mode collapse, producing homogeneous outputs that fail to explore valid solution spaces. We present QD-LLM, a framework for parameter-efficient neuroe…
EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
Dongxin Guo, Jikun Wu, Siu Ming Yiu
Gradient-based preference optimization methods for large language model (LLM) alignment suffer from preference collapse, converging to narrow behavioral modes while neglecting pref…
Do Language Models Know What Not to Say? Causal Evidence for Statistical Preemption in LLMs
Dongxin Guo, Jikun Wu, Siu Ming Yiu
How do learners acquire knowledge of what is unacceptable without negative evidence? Construction Grammar proposes statistical preemption: exposure to a conventional form (e.g., "d…
Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography
Dongxin Guo, Jikun Wu, Siu Ming Yiu
Intermediate layers of large language models (LLMs) best predict human brain responses to language, one of the most robust findings in computational neurolinguistics, yet why remai…
Brain-LLM Alignment Tracks Training Data, Not Typology
Dongxin Guo, Jikun Wu, Siu Ming Yiu
Brain-LLM alignment is well established in English, yet the brain's language network is neuroanatomically universal across languages. Does alignment also generalize cross-linguisti…
Bias by Necessity: Impossibility Theorems for Sequential Processing with Convergent AI and Human Validation
Jikun Wu, Dongxin Guo, Siu-Ming Yiu
Are certain cognitive biases mathematically inevitable consequences of sequential information processing? We prove that primacy effects, anchoring, and order-dependence are archite…