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This research explores strategies for steering the output of large language models (LLMs) towards specific styles, such as sentiment, emotion, or writing style, by adding style vectors to the activations of hidden layers during text generation.
Language models are few-shot learners
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GoEmotions: A Dataset of Fine-Grained Emotions
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Hooks in the headline: Learning to generate headlines with controlled styles
Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, and Peter Szolovits. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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StyleDGPT: Stylized response generation with pre-trained language models
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A survey of controllable text generation using transformer-based pre-trained language models
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Will affective computing emerge from foundation models and general artificial intelligence? a first evaluation of chatgpt
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Emotion english distilroberta-base
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Deep learning for text style transfer: A survey
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A recipe for arbitrary text style transfer with large language models
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting. 2022 · 2022
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Context-aware language modeling for goal-oriented dialogue systems
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Extracting latent steering vectors from pretrained language models
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Measuring and manipulating knowledge representations in language models
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Activation addition: Steering language models without optimization
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Is chatgpt equipped with emotional dialogue capabilities?
Weixiang Zhao, Yanyan Zhao, Xin Lu, Shilong Wang, Yanpeng Tong, and Bing Qin. 2023 · 2023
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