SENSESHIFT: Continuous Sentiment-Controlled Text Generation via Encoder-based Mask Infilling

arXiv:2608.24304 · cs.CL, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/ShawMask/SenseShift

Terminology

• Steering — 22× in this paper · explained in 2 episodes
• Instruction Tuning — 17× in this paper · explained in 2 episodes
• Perplexity — 14× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Controllable Text Generation — 8× in this paper
• Activation Steering — 6× in this paper · explained in 4 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Grounding — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Analyzer — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Addition — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Entity Grounding — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Continuous Language Model Interpolation for Dynamic and Controllable Text Generation
• LLMs Get Lost In Multi-Turn Conversation
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Character-level Convolutional Networks for Text Classification

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