Controllable Natural Language Generation with Contrastive Prefixes
Open accessAbstract
To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator.In this work, we propose a novel lightweight framework for controllable GPT2 (Radford et al., 2019) generation, which utilizes a set of small attribute-specific vectors, called prefixes (Li and Liang, 2021), to steer natural language generation.Different from Li and Liang (2021), where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously, as illustrated in Figure 1.We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control.Experimental results on both singleaspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality.
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In AI data security, key requirements and challenges include ensuring data confidentiality, adhering to consent norms, and safeguarding against vulnerabilities like membership inference attacks.
“In AI data security, key requirements and challenges include ensuring data confidentiality, adhering to consent norms, and safeguarding against vulnerabilities like membership inference attacks [228], [229].”
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CLKSP: medical entity extraction with contrastive learning and knowledgeable soft prompt
Wang, Z., & Yu, J. (2026) · Egyptian Informatics Journal · doi:10.1016/j.eij.2026.101007
TopicTune: A Topic Alignment Approach to Improve LLMs Reasoning on Social Questions
Amirizaniani, M., Ansari, B., Shah, C., & Mashhadi, A. (2026) · International Conference on the Theory of Information Retrieval · doi:10.1145/3805713.3820427
Dong, X., Wang, S., Lin, D., & Hassan, A. E. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3806718
- Authors
- Qian, J., Dong, L., Shen, Y., Wei, F., & Chen, W.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Findings of the Association for Computational Linguistics: ACL 2022
- Published
- 2022
- Pages
- 2912-2924
- OpenAlex
- W4221144473
- Cited by
- 74
- References
- 19
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 11 · 2023: 28 · 2022: 8
- FWCI
- 8.84
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- GPT-2, pre-trained language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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