Papers with attribute control
FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations (2026.acl-long)
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| Challenge: | Recent advances in text-to-speech (TTS) have enabled accurate imitation of reference speech in terms of both speaking style and speaker timbre. |
| Approach: | They propose a zero-shot text-to-speech framework that enables disentangled control of style and timbre by conditioning on two distinct reference utterances. |
| Outcome: | The proposed framework achieves high-fidelity synthesis and competitive zero-shot naturalness while supporting consistent and independent manipulation of style and timbre. |
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework (2024.emnlp-main)
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| Challenge: | RSA-Control is a training-free controllable text generation framework . existing studies rely on fine-tuning pre-trained language models . external components could hurt coherence and accuracy of the model . |
| Approach: | They propose a training-free controllable text generation framework grounded in pragmatics that directs the generation process by recursively reasoning between imaginary speakers and listeners. |
| Outcome: | The proposed framework achieves strong attribute control while maintaining fluency and content consistency. |
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs (2024.findings-acl)
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| Challenge: | Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. |
| Approach: | They propose a pluggable CTG framework for Large Language Models to control text . they use attribute scorers to evaluate attributes of sentences and construct dynamic attribute graphs . |
| Outcome: | The proposed framework achieves a peak improvement of 19.29% over baseline methods in two tasks. |
Miracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control (2023.findings-emnlp)
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| Challenge: | Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. |
| Approach: | They propose a method to generate personalized dialogues using latent-space energy-based models by using a latent space energy-model. |
| Outcome: | The proposed method outperforms baselines in personality controllability and response quality. |
Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification (2024.findings-emnlp)
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Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
| Challenge: | Using a sequence-level constraint, we regularize the LLMtraining by penalizing the KL divergence between the desired output distribution and the LRM’s posterior. |
| Approach: | They propose a constraint learning schema forfine-tuning Large Language Models with attribute control by penalizing the KL divergence be-tween the desired output distribution and the LLM's posterior. |
| Outcome: | The proposed approach improves the performance of large language models while enhancing their utility and generation quality. |
Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music (2026.acl-long)
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| Challenge: | Existing symbolic music generation models represent musical notes as a sequence of attribute tokens with fixed unidirectional dependencies. |
| Approach: | They propose a symbolic music generation framework that adopts a autoregressive and a discrete diffusion architectures for note attributes. |
| Outcome: | The proposed framework improves state-of-the-art models across objective and subjective metrics. |