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cs.CL2026
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
Jingyi He, Haiyan Zhao, Ruxue Shi +4
Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features…
cs.CL2026
FinAnchor: Aligned Multi-Model Representations for Financial Prediction
Zirui He, Huopu Zhang, Yanguang Liu +2
Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embedding…
cs.CL2026
NeuronScope: A Multi-Agent Framework for Explaining Polysemantic Neurons in Language Models
Weiqi Liu, Yongliang Miao, Haiyan Zhao +2
Neuron-level interpretation in large language models (LLMs) is fundamentally challenged by widespread polysemanticity, where individual neurons respond to multiple distinct semanti…