@inproceedings{bandel-etal-2022-quality,
    title = "Quality Controlled Paraphrase Generation",
    author = "Bandel, Elron  and
      Aharonov, Ranit  and
      Shmueli-Scheuer, Michal  and
      Shnayderman, Ilya  and
      Slonim, Noam  and
      Ein-Dor, Liat",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.45",
    doi = "10.18653/v1/2022.acl-long.45",
    pages = "596--609",
    abstract = "Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases. Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree. However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability. Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases. We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline. The models, the code, and the data can be found in \url{https://github.com/IBM/quality-controlled-paraphrase-generation}.",
}

@article{beltagy2020longformer,
  title={Longformer: The long-document transformer},
  author={Beltagy, Iz and Peters, Matthew E and Cohan, Arman},
  journal={arXiv preprint arXiv:2004.05150},
  year={2020}
}

@article{loshchilov2017decoupled,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}

@article{liu2019roberta,
  title={Roberta: A robustly optimized bert pretraining approach},
  author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1907.11692},
  year={2019}
}

@ARTICLE{Jawahar_2020,title={Automatic Detection of Machine Generated Text: A Critical Survey.},year={2020},author={Ganesh Jawahar and Ganesh Jawahar and Muhammad Abdul-Mageed and Muhammad Abdul-Mageed and Laks V. S. Lakshmanan and Laks V. S. Lakshmanan},doi={10.18653/v1/2020.coling-main.208},pmid={null},pmcid={null},mag_id={3114326827},journal={International Conference on Computational Linguistics},abstract={Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.}}

@ARTICLE{Ippolito_2020,title={Automatic Detection of Generated Text is Easiest when Humans are Fooled},year={2020},author={Daphne Ippolito and Daphne Ippolito and Daniel Duckworth and Daniel Duckworth and Chris Callison-Burch and Chris Callison-Burch and Douglas Eck and Douglas Eck},doi={10.18653/v1/2020.acl-main.164},pmid={null},pmcid={null},mag_id={3034287667},journal={Annual Meeting of the Association for Computational Linguistics},abstract={Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions. Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies—top-_k_, nucleus sampling, and untruncated random sampling—and show that improvements in decoding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.}}

@ARTICLE{Mitchell_2023,title={DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability
  Curvature},year={2023},author={Eric Mitchell and Yoonho Lee and Alexander Khazatsky and Christopher D. Manning and Chelsea Finn},doi={10.48550/arxiv.2301.11305},pmid={null},pmcid={null},mag_id={4318351452},journal={International Conference on Machine Learning},abstract={The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection. Specifically, we demonstrate that text sampled from an LLM tends to occupy negative curvature regions of the model's log probability function. Leveraging this observation, we then define a new curvature-based criterion for judging if a passage is generated from a given LLM. This approach, which we call DetectGPT, does not require training a separate classifier, collecting a dataset of real or generated passages, or explicitly watermarking generated text. It uses only log probabilities computed by the model of interest and random perturbations of the passage from another generic pre-trained language model (e.g., T5). We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection, notably improving detection of fake news articles generated by 20B parameter GPT-NeoX from 0.81 AUROC for the strongest zero-shot baseline to 0.95 AUROC for DetectGPT. See https://ericmitchell.ai/detectgpt for code, data, and other project information.}}

@ARTICLE{Badaskar_2008,title={Identifying Real or Fake Articles: Towards better Language Modeling},year={2008},author={Sameer Badaskar and Sameer Badaskar and Sameer Badaskar and Sonali Agarwal and Sachin Agarwal and Shilpa Arora and Shilpa Arora},doi={null},pmid={null},pmcid={null},mag_id={2406990858},journal={International Joint Conference on Natural Language Processing},abstract={The problem of identifying good features for improving conventional language models like trigrams is presented as a classification task in this paper. The idea is to use various syntactic and semantic features extracted from a language for classifying between real-world articles and articles generated by sampling a trigram language model. In doing so, a good accuracy obtained on the classification task implies that the extracted features capture those aspects of the language that a trigram model may not. Such features can be used to improve the existing trigram language models. We describe the results of our experiments on the classification task performed on a Broadcast News Corpus and discuss their effects on language modeling in general.}}

@ARTICLE{Chakraborty_2023,title={On the Possibilities of AI-Generated Text Detection},year={2023},author={Souradip Chakraborty and Amrit Singh Bedi and Sicheng Zhu and Bang An and Dinesh Manocha and Furong Huang},doi={10.48550/arxiv.2304.04736},pmid={null},pmcid={null},mag_id={4364387756},journal={arXiv.org},abstract={Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications. Despite ongoing debate about the feasibility of such differentiation, we present evidence supporting its consistent achievability, except when human and machine text distributions are indistinguishable across their entire support. Drawing from information theory, we argue that as machine-generated text approximates human-like quality, the sample size needed for detection increases. We establish precise sample complexity bounds for detecting AI-generated text, laying groundwork for future research aimed at developing advanced, multi-sample detectors. Our empirical evaluations across multiple datasets (Xsum, Squad, IMDb, and Kaggle FakeNews) confirm the viability of enhanced detection methods. We test various state-of-the-art text generators, including GPT-2, GPT-3.5-Turbo, Llama, Llama-2-13B-Chat-HF, and Llama-2-70B-Chat-HF, against detectors, including oBERTa-Large/Base-Detector, GPTZero. Our findings align with OpenAI's empirical data related to sequence length, marking the first theoretical substantiation for these observations.}}

@ARTICLE{Pu_2023,title={Deepfake Text Detection: Limitations and Opportunities},year={2023},author={Jiameng Pu and Zain Sarwar and Sifat Muhammad Abdullah and Abdullah Rehman and Yoonjin Kim and Parantapa Bhattacharya and Mobin Javed and Bimal Viswanath},doi={10.1109/sp46215.2023.10179387},pmid={null},pmcid={null},mag_id={4385187297},journal={IEEE Symposium on Security and Privacy},abstract={Recent advances in generative models for language have enabled the creation of convincing synthetic text or deepfake text. Prior work has demonstrated the potential for misuse of deepfake text to mislead content consumers. Therefore, deepfake text detection, the task of discriminating between human and machine-generated text, is becoming increasingly critical. Several defenses have been proposed for deepfake text detection. However, we lack a thorough understanding of their real-world applicability. In this paper, we collect deepfake text from 4 online services powered by Transformer-based tools to evaluate the generalization ability of the defenses on content in the wild. We develop several low-cost adversarial attacks, and investigate the robustness of existing defenses against an adaptive attacker. We find that many defenses show significant degradation in performance under our evaluation scenarios compared to their original claimed performance. Our evaluation shows that tapping into the semantic information in the text content is a promising approach for improving the robustness and generalization performance of deepfake text detection schemes.}}

@ARTICLE{Gehrmann_2019,title={GLTR: Statistical Detection and Visualization of Generated Text},year={2019},author={Sebastian Gehrmann and Sebastian Gehrmann and Hendrik Strobelt and Hendrik Strobelt and Gérard Rushton and Alexander M. Rush and Alexander M. Rush and Alexander M. Rush},doi={10.18653/v1/p19-3019},pmid={null},pmcid={null},mag_id={2951080837},journal={Annual Meeting of the Association for Computational Linguistics},abstract={The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by non-experts. In this work, we introduce GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across multiple sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs.}}

@ARTICLE{He_2023,title={MGTBench: Benchmarking Machine-Generated Text Detection},year={2023},author={Xiaotong He and Xinyue Shen and Zeyuan Chen and Michael Backes and Yang Zhang},doi={10.48550/arxiv.2303.14822},pmid={null},pmcid={null},mag_id={4361193535},journal={arXiv.org},abstract={Nowadays large language models (LLMs) have shown revolutionary power in a variety of natural language processing (NLP) tasks such as text classification, sentiment analysis, language translation, and question-answering. In this way, detecting machine-generated texts (MGTs) is becoming increasingly important as LLMs become more advanced and prevalent. These models can generate human-like language that can be difficult to distinguish from text written by a human, which raises concerns about authenticity, accountability, and potential bias. However, existing detection methods against MGTs are evaluated under different model architectures, datasets, and experimental settings, resulting in a lack of a comprehensive evaluation framework across different methodologies In this paper, we fill this gap by proposing the first benchmark framework for MGT detection, named MGTBench. Extensive evaluations on public datasets with curated answers generated by ChatGPT (the most representative and powerful LLMs thus far) show that most of the current detection methods perform less satisfactorily against MGTs. An exceptional case is ChatGPT Detector, which is trained with ChatGPT-generated texts and shows great performance in detecting MGTs. Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods. We envision that MGTBench will serve as a benchmark tool to accelerate future investigations involving the evaluation of state-of-the-art MGT detection methods on their respective datasets and the development of more advanced MGT detection methods. Our source code and datasets are available at https://github.com/xinleihe/MGTBench.}}

@ARTICLE{Guo_2023,title={How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation,
  and Detection},year={2023},author={Biyang Guo and Xin Zhang and Ziyuan Wang and Minqi Jiang and Jinran Nie and Yuxuan Ding and Jianwei Yue and Yupeng Wu},doi={10.48550/arxiv.2301.07597},pmid={null},pmcid={null},mag_id={4317553041},journal={arXiv.org},abstract={The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.}}

@ARTICLE{Zellers_2019,title={Defending Against Neural Fake News},year={2019},author={Rowan Zellers and Rowan Zellers and Ari Holtzman and Ari Holtzman and Hannah Rashkin and Hannah Rashkin and Yonatan Bisk and Yonatan Bisk and Ali Farhadi and Ali Farhadi and Franziska Roesner and Franziska Roesner and Yejin Choi and Yejin Choi},doi={null},pmid={null},pmcid={null},mag_id={2971008823},journal={Neural Information Processing Systems},abstract={Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like 'Link Found Between Vaccines and Autism,' Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias -- and sampling strategies that alleviate its effects -- both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.}}


@article{gu2017trainable,
  title={Trainable greedy decoding for neural machine translation},
  author={Gu, Jiatao and Cho, Kyunghyun and Li, Victor OK},
  journal={arXiv preprint arXiv:1702.02429},
  year={2017}
}

@article{fan2018hierarchical,
  title={Hierarchical neural story generation},
  author={Fan, Angela and Lewis, Mike and Dauphin, Yann},
  journal={arXiv preprint arXiv:1805.04833},
  year={2018}
}

@article{shao2017generating,
  title={Generating high-quality and informative conversation responses with sequence-to-sequence models},
  author={Shao, Louis and Gouws, Stephan and Britz, Denny and Goldie, Anna and Strope, Brian and Kurzweil, Ray},
  journal={arXiv preprint arXiv:1701.03185},
  year={2017}
}

@article{caruana1997multitask,
  title={Multitask learning},
  author={Caruana, Rich},
  journal={Machine learning},
  volume={28},
  pages={41--75},
  year={1997},
  publisher={Springer}
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{christiano2017deep,
  title={Deep reinforcement learning from human preferences},
  author={Christiano, Paul F and Leike, Jan and Brown, Tom and Martic, Miljan and Legg, Shane and Amodei, Dario},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}

@article{gao2022improving,
  title={Improving event representation via simultaneous weakly supervised contrastive learning and clustering},
  author={Gao, Jun and Wang, Wei and Yu, Changlong and Zhao, Huan and Ng, Wilfred and Xu, Ruifeng},
  journal={arXiv preprint arXiv:2203.07633},
  year={2022}
}

@inproceedings{roy-dipta-etal-2023-semantically,
    title = "Semantically-informed Hierarchical Event Modeling",
    author = "Roy Dipta, Shubhashis  and
      Rezaee, Mehdi  and
      Ferraro, Francis",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.31",
    doi = "10.18653/v1/2023.starsem-1.31",
    pages = "353--369",
    abstract = "Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consistsof multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches by up to 8.5{\%}, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.",
}

@article{xu2023contrastive,
  title={Contrastive learning models for sentence representations},
  author={Xu, Lingling and Xie, Haoran and Li, Zongxi and Wang, Fu Lee and Wang, Weiming and Li, Qing},
  journal={ACM Transactions on Intelligent Systems and Technology},
  volume={14},
  number={4},
  pages={1--34},
  year={2023},
  publisher={ACM New York, NY}
}

@inproceedings{wang2023sncse,
  title={SNCSE: contrastive learning for unsupervised sentence embedding with soft negative samples},
  author={Wang, Hao and Dou, Yong},
  booktitle={International Conference on Intelligent Computing},
  pages={419--431},
  year={2023},
  organization={Springer}
}

@article{touvron2023llama,
  title={Llama: Open and efficient foundation language models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}

@article{achiam2023gpt,
  title={Gpt-4 technical report},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}

@inproceedings{semeval2024task8,
    title = {{SemEval}-2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection},
    author = {Wang, Yuxia and Mansurov, Jonibek and Ivanov, Petar and Su, Jinyan and Shelmanov, Artem and Tsvigun, Akim and Whitehouse, Chenxi and Afzal, Osama Mohammed and Mahmoud, Tarek and Puccetti, Giovanni and Arnold, Thomas and Aji, Alham Fikri and Habash, Nizar and Gurevych, Iryna and Nakov, Preslav},
    booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation},
    series = {SemEval 2024},
    year = {2024},
    address = {Mexico City, Mexico},
    month = {June}
}

@article{yang2023gpt,
  title={Gpt can solve mathematical problems without a calculator},
  author={Yang, Zhen and Ding, Ming and Lv, Qingsong and Jiang, Zhihuan and He, Zehai and Guo, Yuyi and Bai, Jinfeng and Tang, Jie},
  journal={arXiv preprint arXiv:2309.03241},
  year={2023}
}

@article{dehouche2021plagiarism,
  title={Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3)},
  author={Dehouche, Nassim},
  journal={Ethics in Science and Environmental Politics},
  volume={21},
  pages={17--23},
  year={2021}
}

@article{cotton2023chatting,
  title={Chatting and cheating: Ensuring academic integrity in the era of ChatGPT},
  author={Cotton, Debby RE and Cotton, Peter A and Shipway, J Reuben},
  journal={Innovations in Education and Teaching International},
  pages={1--12},
  year={2023},
  publisher={Taylor \& Francis}
}

@article{fyfe2023cheat,
  title={How to cheat on your final paper: Assigning AI for student writing},
  author={Fyfe, Paul},
  journal={AI \& SOCIETY},
  volume={38},
  number={4},
  pages={1395--1405},
  year={2023},
  publisher={Springer}
}

@article{oord2018representation,
  title={Representation learning with contrastive predictive coding},
  author={Oord, Aaron van den and Li, Yazhe and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1807.03748},
  year={2018}
}

@article{chechik2010large,
  title={Large scale online learning of image similarity through ranking.},
  author={Chechik, Gal and Sharma, Varun and Shalit, Uri and Bengio, Samy},
  journal={Journal of Machine Learning Research},
  volume={11},
  number={3},
  year={2010}
}

@article{radford2019language,
  title={Language models are unsupervised multitask learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
  journal={OpenAI blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}

@article{neelakantan2022text,
  title={Text and code embeddings by contrastive pre-training},
  author={Neelakantan, Arvind and Xu, Tao and Puri, Raul and Radford, Alec and Han, Jesse Michael and Tworek, Jerry and Yuan, Qiming and Tezak, Nikolas and Kim, Jong Wook and Hallacy, Chris and others},
  journal={arXiv preprint arXiv:2201.10005},
  year={2022}
}

@article{luo2021unsupervised,
  title={Unsupervised document embedding via contrastive augmentation},
  author={Luo, Dongsheng and Cheng, Wei and Ni, Jingchao and Yu, Wenchao and Zhang, Xuchao and Zong, Bo and Liu, Yanchi and Chen, Zhengzhang and Song, Dongjin and Chen, Haifeng and others},
  journal={arXiv preprint arXiv:2103.14542},
  year={2021}
}

@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}