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@inproceedings{wang-demberg-2024-rsa,

title = {RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework},

author = {Yifan Wang and Vera Demberg},

editor = {Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen},

url = {https://aclanthology.org/2024.emnlp-main.318/},

doi = {10.18653/v1/2024.emnlp-main.318},

year  = {2024},

date = {2024-11-01},

booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},

pages = {5561–5582},

publisher = {Association for Computational Linguistics},

address = {Miami, Florida, USA},

abstract = {Despite significant advancements in natural language generation, controlling language models to produce texts with desired attributes remains a formidable challenge. In this work, we introduce RSA-Control, a training-free controllable text generation framework grounded in pragmatics. RSA-Control directs the generation process by recursively reasoning between imaginary speakers and listeners, enhancing the likelihood that target attributes are correctly interpreted by listeners amidst distractors. Additionally, we introduce a self-adjustable rationality parameter, which allows for automatic adjustment of control strength based on context. Our experiments, conducted with two task types and two types of language models, demonstrate that RSA-Control achieves strong attribute control while maintaining language fluency and content consistency. Our code is available at https://github.com/Ewanwong/RSA-Control.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang-demberg-2024-parameter,

title = {A Parameter-Efficient Multi-Objective Approach to Mitigate Stereotypical Bias in Language Models},

author = {Yifan Wang and Vera Demberg},

editor = {Agnieszka Faleńska and Christine Basta and Marta Costa-jussà and Seraphina Goldfarb-Tarrant and Debora Nozza},

url = {https://aclanthology.org/2024.gebnlp-1.1/},

doi = {10.18653/v1/2024.gebnlp-1.1},

year  = {2024},

date = {2024-08-01},

booktitle = {Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)},

pages = {1–19},

publisher = {Association for Computational Linguistics},

address = {Bangkok, Thailand},

abstract = {Pre-trained language models have shown impressive abilities of understanding and generating natural languages. However, they typically inherit undesired human-like bias and stereotypes from training data, which raises concerns about putting these models into use in real-world scenarios. Although prior research has proposed to reduce bias using different fairness objectives, they usually fail to capture different representations of bias and, therefore, struggle with fully debiasing models. In this work, we introduce a multi-objective probability alignment approach to overcome current challenges by incorporating multiple debiasing losses to locate and penalize bias in different forms. Compared to existing methods, our proposed method can more effectively and comprehensively reduce stereotypical bias, and maintains the language ability of pre-trained models at the same time. Besides, we adopt prefix-tuning to optimize fairness objectives, and results show that it can achieve better bias removal than full fine-tuning while requiring much fewer computational resources. Our code and data are available at https://github.com/Ewanwong/debias_NLG.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}