Papers with control accuracy
Focused Prefix Tuning for Controllable Text Generation (2023.acl-short)
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| Challenge: | Existing unannotated attributes could degrade models' performance . focus on the desired attribute can be achieved with focused prefix tuning . |
| Approach: | They propose focused prefix tuning to enable the control to focus on the desired attribute . they propose to reduce the number of unannotated attributes in a controllable text generation dataset . |
| Outcome: | The proposed approach achieves better control accuracy and text fluency than baseline models in single-attribute tasks. |
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. |
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation (2023.emnlp-main)
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| Challenge: | Controlling chatbot utterance generation with multiple attributes is a useful but under-studied problem. |
| Approach: | They propose a framework that possesses strong controllability with a weighted decoding paradigm and improves generation quality with an attribute semantics space. |
| Outcome: | The proposed framework achieves high control accuracy with simultaneous control of 3 aspects while producing interesting and sensible responses even in an out-of-distribution robustness test. |
ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation (2025.emnlp-main)
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| Challenge: | Controllable Dialogue Generation (CDG) enables chatbots to generate responses tailored to desired attributes like emotion and dialog-act. |
| Approach: | They propose a method which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. |
| Outcome: | The proposed method outperforms existing methods on DailyDialog and MultiWOZ datasets while maintaining fluency and grammar. |