% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {$L_1$}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
  url={https://dl.acm.org/doi/abs/10.1145/1273496.1273501}
}

%=========================================================

@article{challenges_in_ODC,
  title={Challenges in building intelligent open-domain dialog systems},
  author={Huang, Minlie and Zhu, Xiaoyan and Gao, Jianfeng},
  journal={ACM Transactions on Information Systems (TOIS)},
  volume={38},
  number={3},
  pages={1--32},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@inproceedings{plato2,
    title = "{PLATO-2}: Towards Building an Open-Domain Chatbot via Curriculum Learning",
    author = "Bao, Siqi  and
      He, Huang  and
      Wang, Fan  and
      Wu, Hua  and
      Wang, Haifeng  and
      Wu, Wenquan  and
      Guo, Zhen  and
      Liu, Zhibin  and
      Xu, Xinchao",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.222",
    doi = "10.18653/v1/2021.findings-acl.222",
    pages = "2513--2525",
}

@article{Lama,
  title={Probing the internal validity of the LLAMA language aptitude tests},
  author={Bokander, Lars and Bylund, Emanuel},
  journal={Language learning},
  volume={70},
  number={1},
  pages={11--47},
  year={2020},
  publisher={Wiley Online Library}
}

@inproceedings{chatGPT,
  title={Augesc: Dialogue augmentation with large language models for emotional support conversation},
  author={Zheng, Chujie and Sabour, Sahand and Wen, Jiaxin and Zhang, Zheng and Huang, Minlie},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  pages={1552--1568},
  year={2023}
}

@inproceedings{blenderbot1,
    title = "Recipes for Building an Open-Domain Chatbot",
    author = "Roller, Stephen  and
      Dinan, Emily  and
      Goyal, Naman  and
      Ju, Da  and
      Williamson, Mary  and
      Liu, Yinhan  and
      Xu, Jing  and
      Ott, Myle  and
      Smith, Eric Michael  and
      Boureau, Y-Lan  and
      Weston, Jason",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-main.24",
    doi = "10.18653/v1/2021.eacl-main.24",
    pages = "300--325",
    abstract = "",
}


@inproceedings{cringe,
    title = "The {CRINGE} Loss: Learning what language not to model",
    author = "Adolphs, Leonard  and
      Gao, Tianyu  and
      Xu, Jing  and
      Shuster, Kurt  and
      Sukhbaatar, Sainbayar  and
      Weston, Jason",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.493",
    doi = "10.18653/v1/2023.acl-long.493",
    pages = "8854--8874",
    abstract = "",
}

@inproceedings{dexperts,
    title = "{DE}xperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts",
    author = "Liu, Alisa  and
      Sap, Maarten  and
      Lu, Ximing  and
      Swayamdipta, Swabha  and
      Bhagavatula, Chandra  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.522",
    doi = "10.18653/v1/2021.acl-long.522",
    pages = "6691--6706",
    abstract = "",
}

@article{LWNTL,
  title={Learn What NOT to Learn: Towards Generative Safety in Chatbots},
  author={Khalatbari, Leila and Bang, Yejin and Su, Dan and Chung, Willy and Ghadimi, Saeed and Sameti, Hossein and Fung, Pascale},
  journal={arXiv preprint arXiv:2304.11220},
  year={2023}
}

@inproceedings{marco,
    title = "Detoxifying Text with {M}a{RC}o: Controllable Revision with Experts and Anti-Experts",
    author = "Hallinan, Skyler  and
      Liu, Alisa  and
      Choi, Yejin  and
      Sap, Maarten",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.21",
    doi = "10.18653/v1/2023.acl-short.21",
    pages = "228--242",
    abstract = "",
}

@article{bb2,
  title={Blender Bot 2.0: An open source chatbot that builds long-term memory and searches the internet},
  author={Weston, Jason and Shuster, Kurt},
  journal={Facebook AI},
  year={2021}
}

@inproceedings{dialoGPT,
    title = "{DIALOGPT} : Large-Scale Generative Pre-training for Conversational Response Generation",
    author = "Zhang, Yizhe  and
      Sun, Siqi  and
      Galley, Michel  and
      Chen, Yen-Chun  and
      Brockett, Chris  and
      Gao, Xiang  and
      Gao, Jianfeng  and
      Liu, Jingjing  and
      Dolan, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-demos.30",
    doi = "10.18653/v1/2020.acl-demos.30",
    pages = "270--278",
    abstract = "",
}

@article{surpas_human1,
  title={Performance of ChatGPT and GPT-4 on neurosurgery written board examinations},
  author={Ali, Rohaid and Tang, Oliver Y and Connolly, Ian D and Sullivan, Patricia L Zadnik and Shin, John H and Fridley, Jared S and Asaad, Wael F and Cielo, Deus and Oyelese, Adetokunbo A and Doberstein, Curtis E and others},
  journal={Neurosurgery},
  pages={10--1227},
  year={2022},
  publisher={LWW}
}

@article{surpas_human2,
  title={Emergent analogical reasoning in large language models},
  author={Webb, Taylor and Holyoak, Keith J and Lu, Hongjing},
  journal={Nature Human Behaviour},
  pages={1--16},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{BAD,
  title={Bot-adversarial dialogue for safe conversational agents},
  author={Xu, Jing and Ju, Da and Li, Margaret and Boureau, Y-Lan and Weston, Jason and Dinan, Emily},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={2950--2968},
  year={2021}
}

@inproceedings{gedi,
    title = "{G}e{D}i: Generative Discriminator Guided Sequence Generation",
    author = "Krause, Ben  and
      Gotmare, Akhilesh Deepak  and
      McCann, Bryan  and
      Keskar, Nitish Shirish  and
      Joty, Shafiq  and
      Socher, Richard  and
      Rajani, Nazneen Fatema",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.424",
    doi = "10.18653/v1/2021.findings-emnlp.424",
    pages = "4929--4952",
    abstract = "",
}

@inproceedings{normative,
    title = "Reducing Non-Normative Text Generation from Language Models",
    author = "Peng, Xiangyu  and
      Li, Siyan  and
      Frazier, Spencer  and
      Riedl, Mark",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.inlg-1.43",
    pages = "374--383",
    abstract = "Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (i.e. in violation of social norms). We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90{\%} accurate when compared to gold-standard human judgements of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative text by 27-61{\%}, depending on the data set.",
}

@inproceedings{Automatic_evaluation,
    title = "Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings",
    author = "Ghazarian, Sarik  and
      Wei, Johnny  and
      Galstyan, Aram  and
      Peng, Nanyun",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-2310",
    doi = "10.18653/v1/W19-2310",
    pages = "82--89",
    abstract = "",
}

@inproceedings{defender,
    title = "Robust Conversational Agents against Imperceptible Toxicity Triggers",
    author = "Mehrabi, Ninareh  and
      Beirami, Ahmad  and
      Morstatter, Fred  and
      Galstyan, Aram",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.204",
    doi = "10.18653/v1/2022.naacl-main.204",
    pages = "2831--2847",
    abstract = "",
}

@inproceedings{fromm2023cross,
  title={Cross-Domain Argument Quality Estimation},
  author={Fromm, Michael and Berrendorf, Max and Faerman, Evgeniy and Seidl, Thomas},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  pages={13435--13448},
  year={2023}
}

@inproceedings{parlai_tox,
    title = "{P}arl{AI}: A Dialog Research Software Platform",
    author = "Miller, Alexander  and
      Feng, Will  and
      Batra, Dhruv  and
      Bordes, Antoine  and
      Fisch, Adam  and
      Lu, Jiasen  and
      Parikh, Devi  and
      Weston, Jason",
    editor = "Specia, Lucia  and
      Post, Matt  and
      Paul, Michael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-2014",
    doi = "10.18653/v1/D17-2014",
    pages = "79--84",
    abstract = "",
}

@article{taxonomy,
  title={A taxonomy and review of generalization research in NLP},
  author={Hupkes, Dieuwke and Giulianelli, Mario and Dankers, Verna and Artetxe, Mikel and Elazar, Yanai and Pimentel, Tiago and Christodoulopoulos, Christos and Lasri, Karim and Saphra, Naomi and Sinclair, Arabella and others},
  journal={Nature Machine Intelligence},
  volume={5},
  number={10},
  pages={1161--1174},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{TowardsAO,
  title={Towards Answering Open-ended Ethical Quandary Questions},
  author={Yejin Bang and Nayeon Lee and Tiezheng Yu and Leila Khalatbari and Yan Xu and Samuel Cahyawijaya and Dan Su and Bryan Wilie and Romain Barraud and Elham J. Barezi and Andrea Madotto and Hayden Kee and Pascale Fung},
  year={2022},
  url={https://api.semanticscholar.org/CorpusID:256459930}
}

@inproceedings{click,
    title = "Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning",
    author = "Zheng, Chujie  and
      Ke, Pei  and
      Zhang, Zheng  and
      Huang, Minlie",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.65",
    doi = "10.18653/v1/2023.findings-acl.65",
    pages = "1022--1040",
    abstract = "It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition. We introduce Leo for controllable text generation, which needs no modification to the model architecture and facilitates out-of-the-box use of trained models. It employs a contrastive loss on sequence likelihood, which fundamentally decreases the generation probability of negative samples (i.e., generations with undesirable attributes). It also adopts a novel likelihood ranking-based strategy to construct contrastive samples from model generations. On the tasks of language detoxification, sentiment steering, and repetition reduction, we show that Leo outperforms strong baselines of controllable text generation and demonstrate the superiority of Leo{'}s sample construction strategy.",
}

@inproceedings{unlikelihood,
    title = "Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning",
    author = "Lagutin, Evgeny  and
      Gavrilov, Daniil  and
      Kalaidin, Pavel",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-main.123",
    doi = "10.18653/v1/2021.eacl-main.123",
    pages = "1432--1441",
    abstract = "Likelihood training and maximization-based decoding result in dull and repetitive generated texts even when using powerful language models (Holtzman et al., 2019). Adding a loss function for regularization was shown to improve text generation output by helping avoid unwanted properties, such as contradiction or repetition (Li at al., 2020). In this work, we propose fine-tuning a language model by using policy gradient reinforcement learning, directly optimizing for better generation. We apply this approach to minimizing repetition in generated text, and show that, when combined with unlikelihood training (Welleck et al., 2020), our method further reduces repetition without impacting the language model quality. We also evaluate other methods for improving generation at training and decoding time, and compare them using various metrics aimed at control for better text generation output.",
}

@article{behatta1,
  title={The quality of training sample estimates of the bhattacharyya coefficient},
  author={Djouadi, Abdelhamid and Snorrason, Oe. and Garber, Frederick D},
  journal={IEEE Transactions on Pattern analysis and machine intelligence},
  volume={12},
  number={1},
  pages={92--97},
  year={1990},
  publisher={IEEE}
}

@article{behatta2,
  title={Depth-based human action recognition using histogram of templates},
  author={Younsi, Merzouk and Yesli, Samir and Diaf, Moussa},
  journal={Multimedia Tools and Applications},
  pages={1--35},
  year={2023},
  publisher={Springer}
}

@article{kog,
  title={On the relation between KL divergence and transfer learning performance on causality extraction tasks},
  author={Gopalakrishnan, Seethalakshmi and Chen, Victor Zitian and Dou, Wenwen and Zadrozny, Wlodek},
  journal={Natural Language Processing Journal},
  pages={100055},
  year={2024},
  publisher={Elsevier}
}

@inproceedings{adversary,
    title = "Text Detoxification using Large Pre-trained Neural Models",
    author = "Dale, David  and
      Voronov, Anton  and
      Dementieva, Daryna  and
      Logacheva, Varvara  and
      Kozlova, Olga  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.629",
    doi = "10.18653/v1/2021.emnlp-main.629",
    pages = "7979--7996",
    abstract = "We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.",
}

@inproceedings{implicit1,
  title={FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation},
  author={Chen, Minghui and Jiang, Meirui and Dou, Qi and Wang, Zehua and Li, Xiaoxiao},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={318--328},
  year={2023},
  organization={Springer}
}

@phdthesis{,
  title={Towards Deep Learning with Competing Generalisation Objectives},
  author={Rusu, Andrei-Alexandru},
  year={2023},
  school={UCL (University College London)}
}

@inproceedings{BBB,
  title={Build it, break it, fix it: Contesting secure development},
  author={Ruef, Andrew and Hicks, Michael and Parker, James and Levin, Dave and Mazurek, Michelle L and Mardziel, Piotr},
  booktitle={Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security},
  pages={690--703},
  year={2016}
}
@article{flat,
  title={Relative flatness and generalization},
  author={Petzka, Henning and Kamp, Michael and Adilova, Linara and Sminchisescu, Cristian and Boley, Mario},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={18420--18432},
  year={2021}
}

@inproceedings{prosocialdialog,
    title = "{P}rosocial{D}ialog: A Prosocial Backbone for Conversational Agents",
    author = "Kim, Hyunwoo  and
      Yu, Youngjae  and
      Jiang, Liwei  and
      Lu, Ximing  and
      Khashabi, Daniel  and
      Kim, Gunhee  and
      Choi, Yejin  and
      Sap, Maarten",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.267",
    doi = "10.18653/v1/2022.emnlp-main.267",
    pages = "4005--4029",
    abstract = "Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales. With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.",
}

@article{prosocial,
  title={Prosocialdialog: A prosocial backbone for conversational agents},
  author={Kim, Hyunwoo and Yu, Youngjae and Jiang, Liwei and Lu, Ximing and Khashabi, Daniel and Kim, Gunhee and Choi, Yejin and Sap, Maarten},
  journal={arXiv preprint arXiv:2205.12688},
  year={2022}
}