Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering
Li Zheng, Xin Zhang, Shuyi He, Fei Li, Chong Teng, Jiangming Yang, Donghong Ji, Zhuang Li

TL;DR
This paper investigates the internal neuron mechanisms of LLMs for emotion and rhetoric, proposing a new neuron identification and adaptive masking framework to enable precise control over these expressions.
Contribution
It introduces a systematic neuron recognition framework and adaptive masking method for fine-grained steering of emotion and rhetoric in LLMs, addressing previous limitations.
Findings
Effective neuron identification for emotion and rhetoric categories
Reliable causal validation of neuron functionality achieved
Enhanced emotion and rhetoric expression control demonstrated
Abstract
Accurate comprehension and controllable generation of emotion and rhetoric are pivotal for enhancing the reasoning capabilities of large language models (LLMs). Existing studies mostly rely on external optimizations, lacking in-depth exploration of internal representation mechanisms, thus failing to achieve fine-grained steering at the neuron level. A handful of works on neurons are confined to emotions, neglecting rhetoric neurons and their intrinsic connections. Traditional neuron masking also exhibits counterintuitive phenomena, making reliable verification of neuron functionality infeasible. To address these issues, we systematically investigate the neurons representation mechanisms and inherent associations of 6 emotion categories and 4 core rhetorical devices. We propose a neuron identification framework that integrates multi-dimensional screening, and design an adaptive masking…
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