---
title: "Who cites Xiaoxiao Guo — 359 papers of our library"
description: "359 papers of the Elamur Research library cite the works of Xiaoxiao Guo, found in their bibliographies; the most cited first."
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---
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# Who cites Xiaoxiao Guo

365 citations of their works from 359 papers of our library (of the 149,439 whose bibliographies we read) · 35 with a page here, the most cited first · [← Xiaoxiao Guo](https://md.research.elamur.ai/free/en/authors/xiaoxiao-guo/)

1. **[Making Pre-trained Language Models Better Few-shot Learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,987 citations*
2. **[Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Timo Schick, Hinrich Schütze*2021 · 1,817 citations*
3. **[Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wu-2019-transferable-multi-domain-state-generator/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, et al.*2019 · 628 citations*
4. **[A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 401 citations*
5. [**Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551**Danijar Hafner, Timothy Lillicrap, Ian Fischer, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/hafner-2018-learning-latent-dynamics-planning-pixels/)
6. **[Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents](https://md.research.elamur.ai/free/en/papers/machado-2017-revisiting-arcade-learning-environment-evaluation/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, et al.*2017 · 393 citations*
7. [**Retrieval Augmentation Reduces Hallucination in Conversation arXiv:2104.07567**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
8. [**HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data arXiv:2004.07347**Wenhu Chen, Hanwen Zha, Zhiyu Chen, et al.*2020 · 382 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/)
9. [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 347 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
10. **[RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 338 citations*
11. [**Reinforcement Learning with Unsupervised Auxiliary Tasks arXiv:1611.05397**Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, et al.*2016 · 319 citations*](https://md.research.elamur.ai/free/en/papers/jaderberg-2016-reinforcement-learning-unsupervised-auxiliary-tasks/)
12. **[Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://md.research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jue Wang, Wei Lu*2020 · 288 citations*
13. **[Induction Networks for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/geng-2019-induction-networks-few-shot-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruiying Geng, Binhua Li, Yongbin Li, et al.*2019 · 263 citations*
14. **[Personalizing Dialogue Agents via Meta-Learning](https://md.research.elamur.ai/free/en/papers/madotto-2019-personalizing-dialogue-agents-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, et al.*2019 · 246 citations*
15. **[Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Oshin Agarwal, Heming Ge, Siamak Shakeri, et al.*2021 · 239 citations*
16. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
17. [**Grounded Language Learning in a Simulated 3D World arXiv:1706.06551**Karl Moritz Hermann, Felix Hill, Simon Green, et al.*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2017-grounded-language-learning-simulated-3d/)
18. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ning Ding, Yulin Chen, Xu Han, et al.*2022 · 181 citations*
19. **[Zero-Shot Cross-Lingual Transfer with Meta Learning](https://md.research.elamur.ai/free/en/papers/nooralahzadeh-2020-zero-shot-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, et al.*2020 · 171 citations*
20. **[Continual Relation Learning via Episodic Memory Activation and Reconsolidation](https://md.research.elamur.ai/free/en/papers/han-2020-continual-relation-learning-episodic-memory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Han, Yi Dai, Tianyu Gao, et al.*2020 · 152 citations*
21. [**Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering arXiv:1911.03868**Sewon Min, Danqi Chen, Luke Zettlemoyer, et al.*2019 · 133 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-knowledge-guided-text-retrieval-reading/)
22. **[Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/chen-2020-open-domain-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danqi Chen, Wen-tau Yih*2020 · 129 citations*
23. [**Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model arXiv:1912.09637**Wenhan Xiong, Jingfei Du, William Yang Wang, et al.*2019 · 117 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2019-pretrained-encyclopedia-weakly-supervised-knowledge/)
24. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
25. [**Simulating Action Dynamics with Neural Process Networks arXiv:1711.05313**Antoine Bosselut, Omer Levy, Ari Holtzman, et al.*2017 · 102 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2017-simulating-action-dynamics-neural-process/)
26. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations*
27. [**English Machine Reading Comprehension Datasets: A Survey**Daria Dzendzik, Jennifer Foster, Carl Vogel*2021 · 54 citations*](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
28. **[Contextualization Distillation from Large Language Model for Knowledge Graph Completion](https://md.research.elamur.ai/free/en/papers/li-2024-contextualization-distillation-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dawei Li, Zhen Tan, Tianlong Chen, et al.*2024 · 30 citations*
29. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
30. [**Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings arXiv:2410.13153**Krishno Dey, Prerona Tarannum, Md. Arid Hasan, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/dey-2024-better-ask-english-evaluation-large/)
31. [**ConPET: Continual Parameter-Efficient Tuning for Large Language Models arXiv:2309.14763**Chenyang Song, Xu Han, Zheni Zeng, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-conpet-continual-parameter-efficient-tuning/)
32. **[Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons](https://md.research.elamur.ai/free/en/papers/leng-2025-understanding-multi-task-learning-generalization/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yongqi Leng, Deyi Xiong*2025 · 9 citations*
33. **[Exploring Continual Learning of Compositional Generalization in NLI](https://md.research.elamur.ai/free/en/papers/fu-2024-exploring-continual-learning-compositional-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xiyan Fu, Anette Frank*2024 · 5 citations*
34. **[QA-KGNet: Language Model-driven Knowledge Graph Question-answering Model (QA-KGNet: 一种语言模型驱动的知识图谱问答模型)](https://md.research.elamur.ai/free/en/papers/qiao-2023-qa-kgnet-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Shaojie Qiao (乔少杰), Guoping Yang (杨国平), Yong Yu (于泳), et al.*2023 · 1 citation*
35. **[MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/sun-2026-mpboco-multimodal-prompt-based-boundary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanglu Sun, Xinyu Liu, Lili Liang, et al.*2026*

324 more citing papers of our library have no page here yet.

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