Work

Controllable Affective Generation via Latent Vector Steering

Year: 2026

Type: conference-paper

Abstract: Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affectsensitive applications.In this paper, we propose EmoVec, a lightweight framework for controllable affective generation via latent vector steering.EmoVec extracts emotion-specific directions from paired neutral and emotionconditioned responses using contrastive activation addition, and further refines them through task-specific debiasing and principal subspace removal.During inference, these vectors are injected into the final residual stream with static or scenario-adaptive scaling, enabling continuous control over emotional intensity without updating model weights.Experiments across three LLMs and eight emotions show that EmoVec consistently improves emotional salience while largely preserving semantic content, fluency, and coherence.Ablation studies and human evaluation further confirm the effectiveness of vector purification and adaptive scaling, establishing EmoVec as a practical inference-time method for affective control in deployed LLMs.Code and data are available at https://github.com/chicosirius/ EmoVec.

Source: Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)

DOI: 10.18653/2026.findings-emnlp.1707

Authors: Xixian Yong, Siyuan Chang, Yingying Zhang, Xian Wu, Xiao Zhou

Institutions: Renmin University of China, Tencent (China)

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Beijing Nova Program

OpenAlex ID: W7204494142