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@inproceedings{DBLP:conf:acl:ChenZZK022,
author = {Yanda Chen and
Ruiqi Zhong and
Sheng Zha and
George Karypis and
He He},
editor = {Smaranda Muresan and
Preslav Nakov and
Aline Villavicencio},
title = {Meta-learning via Language Model In-context Tuning},
booktitle = {ACL},
pages = {719--730},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.acl-long.53},
doi = {10.18653/v1/2022.acl-long.53},
timestamp = {Mon, 01 Aug 2022 16:27:45 +0200},
biburl = {https://dblp.org/rec/conf/acl/ChenZZK022.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords = {In-Context Learning, Meta Learning},
plm={BERT, DeBERTa, GPT-2}
}
@string(DBLP:conf:acl:ChenZZK022="This paper proposes in-context tuning, which recasts task adaptation and prediction as a simple sequence prediction problem: to form the input sequence, concatenate the task instruction, labeled in-context examples, and the target input to predict; to meta train the model to learn from in-context examples, finetune a PLM to predict the target label given the input sequence on a collection of tasks (very similar to MetaICL). On LAMA and BinaryClfs, the proposed method outperforms MAML.")
@article{DBLP:journals/corr/abs-2210-12810,
author = {Xingyao Wang and
Sha Li and
Heng Ji},
title = {Code4Struct: Code Generation for Few-Shot Structured Prediction from
Natural Language},
journal = {CoRR},
volume = {abs/2210.12810},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2210.12810},
doi = {10.48550/arXiv.2210.12810},
eprinttype = {arXiv},
eprint = {2210.12810},
timestamp = {Fri, 28 Oct 2022 14:21:57 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2210-12810.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords = {Program and Code Generation}
}
@inproceedings{DBLP:conf/cvpr/0002ZL0SRSPDP22,
author = {Zifeng Wang and
Zizhao Zhang and
Chen{-}Yu Lee and
Han Zhang and
Ruoxi Sun and
Xiaoqi Ren and
Guolong Su and
Vincent Perot and
Jennifer G. Dy and
Tomas Pfister},
title = {Learning to Prompt for Continual Learning},
booktitle = {CVPR},
pages = {139--149},
publisher = {{IEEE}},
year = {2022},
url = {https://doi.org/10.1109/CVPR52688.2022.00024},
doi = {10.1109/CVPR52688.2022.00024},
timestamp = {Tue, 04 Oct 2022 17:56:08 +0200},
biburl = {https://dblp.org/rec/conf/cvpr/0002ZL0SRSPDP22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords = {Continual Learning, Prompt Engineering}
}
@inproceedings{DBLP:conf/emnlp/Hu0X0SO22,
author = {Yushi Hu and
Chia{-}Hsuan Lee and
Tianbao Xie and
Tao Yu and
Noah A. Smith and
Mari Ostendorf},
editor = {Yoav Goldberg and
Zornitsa Kozareva and
Yue Zhang},
title = {In-Context Learning for Few-Shot Dialogue State Tracking},
booktitle = {Findings of the Association for Computational Linguistics: {EMNLP}
2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022},
pages = {2627--2643},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://aclanthology.org/2022.findings-emnlp.193},
timestamp = {Tue, 07 Feb 2023 17:10:51 +0100},
biburl = {https://dblp.org/rec/conf/emnlp/Hu0X0SO22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/emnlp/GuptaJYMEB22,
author = {Prakhar Gupta and
Cathy Jiao and
Yi{-}Ting Yeh and
Shikib Mehri and
Maxine Esk{\'{e}}nazi and
Jeffrey P. Bigham},
editor = {Yoav Goldberg and
Zornitsa Kozareva and
Yue Zhang},
title = {InstructDial: Improving Zero and Few-shot Generalization in Dialogue
through Instruction Tuning},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing, {EMNLP} 2022, Abu Dhabi, United Arab Emirates,
December 7-11, 2022},
pages = {505--525},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://aclanthology.org/2022.emnlp-main.33},
timestamp = {Tue, 07 Feb 2023 17:10:51 +0100},
biburl = {https://dblp.org/rec/conf/emnlp/GuptaJYMEB22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-2210-07128,
author = {Aman Madaan and
Shuyan Zhou and
Uri Alon and
Yiming Yang and
Graham Neubig},
title = {Language Models of Code are Few-Shot Commonsense Learners},
journal = {CoRR},
volume = {abs/2210.07128},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2210.07128},
doi = {10.48550/arXiv.2210.07128},
eprinttype = {arXiv},
eprint = {2210.07128},
timestamp = {Tue, 18 Oct 2022 15:06:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2210-07128.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords = {Program and Code Generation}
}
@inproceedings{DBLP:conf/naacl/WebsonP22,
author = {Albert Webson and
Ellie Pavlick},
editor = {Marine Carpuat and
Marie{-}Catherine de Marneffe and
Iv{\'{a}}n Vladimir Meza Ru{\'{\i}}z},
title = {Do Prompt-Based Models Really Understand the Meaning of Their Prompts?},
booktitle = {NAACL},
pages = {2300--2344},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.naacl-main.167},
doi = {10.18653/v1/2022.naacl-main.167},
timestamp = {Mon, 01 Aug 2022 16:28:03 +0200},
biburl = {https://dblp.org/rec/conf/naacl/WebsonP22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords= {Prompt Engineering}
}
@inproceedings{DBLP:conf:naacl:MinLZH22,
author = {Sewon Min and
Mike Lewis and
Luke Zettlemoyer and
Hannaneh Hajishirzi},
editor = {Marine Carpuat and
Marie{-}Catherine de Marneffe and
Iv{\'{a}}n Vladimir Meza Ru{\'{\i}}z},
title = {MetaICL: Learning to Learn In Context},
booktitle = {NAACL},
pages = {2791--2809},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.naacl-main.201},
doi = {10.18653/v1/2022.naacl-main.201},
timestamp = {Mon, 01 Aug 2022 16:28:01 +0200},
biburl = {https://dblp.org/rec/conf/naacl/MinLZH22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords= {In-Context Learning, Meta Learning},
code={https://github.com/facebookresearch/MetaICL},
plm={GPT-2}
}
@string(DBLP:conf:naacl:MinLZH22="MetaICL proposes a supervised meta-training framework to enable LMs to more effectively learn a new task in context. In MetaICL, each meta-training example includes several training examples from one task that will be presented together as a single sequence to the LM, and the prediction of the final example is used to calculate the loss.")
@article{DBLP:journals/corr/abs-2209-12356,
author = {Tanya Goyal and
Junyi Jessy Li and
Greg Durrett},
title = {News Summarization and Evaluation in the Era of {GPT-3}},
journal = {NeurIPS},
volume = {abs/2209.12356},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2209.12356},
doi = {10.48550/arXiv.2209.12356},
eprinttype = {arXiv},
eprint = {2209.12356},
timestamp = {Thu, 06 Oct 2022 14:41:30 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2209-12356.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={AIGC}
}
@article{DBLP:journals:corr:abs-2210-09338,
author = {Michihiro Yasunaga and
Antoine Bosselut and
Hongyu Ren and
Xikun Zhang and
Christopher D. Manning and
Percy Liang and
Jure Leskovec},
title = {Deep Bidirectional Language-Knowledge Graph Pretraining},
journal = {NeurIPS},
volume = {abs/2210.09338},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2210.09338},
doi = {10.48550/arXiv.2210.09338},
eprinttype = {arXiv},
eprint = {2210.09338},
timestamp = {Mon, 24 Oct 2022 18:10:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2210-09338.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Knowledge Enhanced,Knowledge Graph Embedding}
}
@article{DBLP:journals:corr:abs-2209-01975,
author = {Hongjin Su and
Jungo Kasai and
Chen Henry Wu and
Weijia Shi and
Tianlu Wang and
Jiayi Xin and
Rui Zhang and
Mari Ostendorf and
Luke Zettlemoyer and
Noah A. Smith and
Tao Yu},
title = {Selective Annotation Makes Language Models Better Few-Shot Learners},
journal = {CoRR},
volume = {abs/2209.01975},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2209.01975},
doi = {10.48550/arXiv.2209.01975},
eprinttype = {arXiv},
eprint = {2209.01975},
timestamp = {Tue, 27 Sep 2022 08:13:21 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2209-01975.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Selective Annotation, In-Context Learning},
code={https://github.com/HKUNLP/icl-selective-annotation},
plm={SBERT, GPT-J, GPT-Neo, GPT-3, Codex, OPT}
}
@String(DBLP:journals:corr:abs-2209-01975="This paper proposes a graph-based selective annotation method named vote-k to
(1) select a pool of examples to annotate from unlabeled data,
(2) retrieve prompts (contexts) from the annotated data pool for in-context learning.
Specifically, the selection method first selects a small set of unlabeled examples iteratively and then labels them to serve as contexts for LLMs to predict the labels of the rest unlabeled data. The method selects the predictions with highest confidence (log probability of generation output) to fill up the selective annotation pool.")
@article{DBLP:journals/pvldb/ChaiLTLL22,
author = {Chengliang Chai and
Jiabin Liu and
Nan Tang and
Guoliang Li and
Yuyu Luo},
title = {Selective Data Acquisition in the Wild for Model Charging},
journal = {VLDB},
volume = {15},
number = {7},
pages = {1466--1478},
year = {2022},
url = {https://www.vldb.org/pvldb/vol15/p1466-li.pdf},
timestamp = {Wed, 29 Jun 2022 11:10:54 +0200},
biburl = {https://dblp.org/rec/journals/pvldb/ChaiLTLL22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Selective Annotation}
}
@article{DBLP:journals/corr/abs-2212-09420,
author = {Daoguang Zan and
Bei Chen and
Fengji Zhang and
Dianjie Lu and
Bingchao Wu and
Bei Guan and
Yongji Wang and
Jian{-}Guang Lou},
title = {When Neural Model Meets NL2Code: {A} Survey},
journal = {CoRR},
volume = {abs/2212.09420},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2212.09420},
doi = {10.48550/arXiv.2212.09420},
eprinttype = {arXiv},
eprint = {2212.09420},
timestamp = {Tue, 03 Jan 2023 15:59:43 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2212-09420.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Program and Code Generation, Survey}
}
@article{DBLP:journals:corr:abs-2212-13428,
author = {Chaoqi Zhen and
Yanlei Shang and
Xiangyu Liu and
Yifei Li and
Yong Chen and
Dell Zhang},
title = {A Survey on Knowledge-Enhanced Pre-trained Language Models},
journal = {TKDE},
volume = {abs/2212.13428},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2212.13428},
doi = {10.48550/arXiv.2212.13428},
eprinttype = {arXiv},
eprint = {2212.13428},
timestamp = {Wed, 04 Jan 2023 16:01:37 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2212-13428.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Knowledge Enhanced, Survey}
}
@article{yi2022review,
title={Review of Knowledge-Enhanced Pre-trained Language Models},
author={Yi, HAN and Linbo, QIAO and Dongsheng, LI and Xiangke, LIAO},
journal={FCST},
volume={16},
number={7},
pages={1439},
year={2022},
url={https://doi.org/10.3778/j.issn.1673-9418.2108105},
keywords={Knowledge Enhanced}
}
@article{DBLP:journals:corr:abs-2302-04023,
author = {Yejin Bang and
Samuel Cahyawijaya and
Nayeon Lee and
Wenliang Dai and
Dan Su and
Bryan Wilie and
Holy Lovenia and
Ziwei Ji and
Tiezheng Yu and
Willy Chung and
Quyet V. Do and
Yan Xu and
Pascale Fung},
title = {A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning,
Hallucination, and Interactivity},
journal = {CoRR},
volume = {abs/2302.04023},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2302.04023},
doi = {10.48550/arXiv.2302.04023},
eprinttype = {arXiv},
eprint = {2302.04023},
timestamp = {Fri, 10 Feb 2023 12:26:38 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2302-04023.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Evaluation}
}
@String(DBLP:journals:corr:abs-2302-04023="本文提出了一个使用公开数据集定量评估交互式LLM（如ChatGPT）的框架。我们使用涵盖8个不同的常见NLP应用任务的21个数据集对ChatGPT进行了广泛的技术评估。我们基于这些数据集和一个新设计的多模态数据集评估了ChatGPT的多任务、多语言和多模态方面。")
@article{DBLP:journals:corr:abs-2301-12810,
author = {Roi Cohen and
Mor Geva and
Jonathan Berant and
Amir Globerson},
title = {Crawling the Internal Knowledge-Base of Language Models},
journal = {EACL},
volume = {abs/2301.12810},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2301.12810},
doi = {10.48550/arXiv.2301.12810},
eprinttype = {arXiv},
eprint = {2301.12810},
timestamp = {Wed, 01 Feb 2023 14:38:31 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2301-12810.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Knowledge Generation}
}
@String(DBLP:journals:corr:abs-2301-12810="本文提出一种从语言模型中提取结构化知识图谱的方法；使用专门设计的提示来控制提取过程中的精度和召回率；在GPT-3上进行了评估，显示了高精确度的结果。")
@article{DBLP:journals/corr/abs-2211-09110,
author = {Percy Liang and
Rishi Bommasani and
Tony Lee and
Dimitris Tsipras and
Dilara Soylu and
Michihiro Yasunaga and
Yian Zhang and
Deepak Narayanan and
Yuhuai Wu and
Ananya Kumar and
Benjamin Newman and
Binhang Yuan and
Bobby Yan and
Ce Zhang and
Christian Cosgrove and
Christopher D. Manning and
Christopher R{\'{e}} and
Diana Acosta{-}Navas and
Drew A. Hudson and
Eric Zelikman and
Esin Durmus and
Faisal Ladhak and
Frieda Rong and
Hongyu Ren and
Huaxiu Yao and
Jue Wang and
Keshav Santhanam and
Laurel J. Orr and
Lucia Zheng and
Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
Mirac Suzgun and
Nathan Kim and
Neel Guha and
Niladri S. Chatterji and
Omar Khattab and
Peter Henderson and
Qian Huang and
Ryan Chi and
Sang Michael Xie and
Shibani Santurkar and
Surya Ganguli and
Tatsunori Hashimoto and
Thomas Icard and
Tianyi Zhang and
Vishrav Chaudhary and
William Wang and
Xuechen Li and
Yifan Mai and
Yuhui Zhang and
Yuta Koreeda},
title = {Holistic Evaluation of Language Models},
journal = {CoRR},
volume = {abs/2211.09110},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2211.09110},
doi = {10.48550/arXiv.2211.09110},
eprinttype = {arXiv},
eprint = {2211.09110},
timestamp = {Wed, 23 Nov 2022 18:03:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2211-09110.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Evaluation}
}
@article{DBLP:journals/corr/abs-2302-00923,
author = {Zhuosheng Zhang and
Aston Zhang and
Mu Li and
Hai Zhao and
George Karypis and
Alex Smola},
title = {Multimodal Chain-of-Thought Reasoning in Language Models},
journal = {CoRR},
volume = {abs/2302.00923},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2302.00923},
doi = {10.48550/arXiv.2302.00923},
eprinttype = {arXiv},
eprint = {2302.00923},
timestamp = {Thu, 09 Feb 2023 16:11:17 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2302-00923.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Chain of Thought}
}
@article{DBLP:journals:corr:abs-2301-00303,
author = {Hangfeng He and
Hongming Zhang and
Dan Roth},
title = {Rethinking with Retrieval: Faithful Large Language Model Inference},
journal = {CoRR},
volume = {abs/2301.00303},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2301.00303},
doi = {10.48550/arXiv.2301.00303},
eprinttype = {arXiv},
eprint = {2301.00303},
timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2301-00303.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Chain of Thought}
}
@String(DBLP:journals:corr:abs-2301-00303="本文通过用GPT-3在三个复杂的推理任务：常识推理，时间推理和表格推理上进行大量实验来评估RR的有效性。结果表明，RR可以产生更忠实的解释，并提高LLM的性能。")
@inproceedings{DBLP:conf/aaai/VilaresSSG20,
author = {David Vilares and
Michalina Strzyz and
Anders S{\o}gaard and
Carlos G{\'{o}}mez{-}Rodr{\'{\i}}guez},
title = {Parsing as Pretraining},
booktitle = {AAAI},
pages = {9114--9121},
publisher = {{AAAI} Press},
year = {2020},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/6446},
timestamp = {Mon, 07 Mar 2022 16:57:52 +0100},
biburl = {https://dblp.org/rec/conf/aaai/VilaresSSG20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Others}
}
// from Chen Yongrui
@article{DBLP:journals/corr/abs-2107-03374,
author = {Mark Chen and
Jerry Tworek and
Heewoo Jun and
Qiming Yuan and
Henrique Pond{\'{e}} de Oliveira Pinto and
Jared Kaplan and
Harrison Edwards and
Yuri Burda and
Nicholas Joseph and
Greg Brockman and
Alex Ray and
Raul Puri and
Gretchen Krueger and
Michael Petrov and
Heidy Khlaaf and
Girish Sastry and
Pamela Mishkin and
Brooke Chan and
Scott Gray and
Nick Ryder and
Mikhail Pavlov and
Alethea Power and
Lukasz Kaiser and
Mohammad Bavarian and
Clemens Winter and
Philippe Tillet and
Felipe Petroski Such and
Dave Cummings and
Matthias Plappert and
Fotios Chantzis and
Elizabeth Barnes and
Ariel Herbert{-}Voss and
William Hebgen Guss and
Alex Nichol and
Alex Paino and
Nikolas Tezak and
Jie Tang and
Igor Babuschkin and
Suchir Balaji and
Shantanu Jain and
William Saunders and
Christopher Hesse and
Andrew N. Carr and
Jan Leike and
Joshua Achiam and
Vedant Misra and
Evan Morikawa and
Alec Radford and
Matthew Knight and
Miles Brundage and
Mira Murati and
Katie Mayer and
Peter Welinder and
Bob McGrew and
Dario Amodei and
Sam McCandlish and
Ilya Sutskever and
Wojciech Zaremba},
title = {Evaluating Large Language Models Trained on Code},
journal = {CoRR},
volume = {abs/2107.03374},
year = {2021},
url = {https://arxiv.org/abs/2107.03374},
eprinttype = {arXiv},
eprint = {2107.03374},
timestamp = {Thu, 09 Feb 2023 14:04:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-03374.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Evaluation, Program and Code Generation}
}
@article{DBLP:journals/corr/abs-2301-12868,
author = {Terry Yue Zhuo and
Zhuang Li and
Yujin Huang and
Yuan{-}Fang Li and
Weiqing Wang and
Gholamreza Haffari and
Fatemeh Shiri},
title = {On Robustness of Prompt-based Semantic Parsing with Large Pre-trained
Language Model: An Empirical Study on Codex},
journal = {CoRR},
volume = {abs/2301.12868},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2301.12868},
doi = {10.48550/arXiv.2301.12868},
eprinttype = {arXiv},
eprint = {2301.12868},
timestamp = {Fri, 03 Feb 2023 10:27:08 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2301-12868.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Prompt Engineering, Program and Code Generation}
}
@inproceedings{DBLP:conf/iclr/LiuSPGSKS18,
author = {Peter J. Liu and
Mohammad Saleh and
Etienne Pot and
Ben Goodrich and
Ryan Sepassi and
Lukasz Kaiser and
Noam Shazeer},
title = {Generating Wikipedia by Summarizing Long Sequences},
booktitle = {ICLR},
publisher = {OpenReview.net},
year = {2018},
url = {https://openreview.net/forum?id=Hyg0vbWC-},
timestamp = {Thu, 25 Jul 2019 14:25:42 +0200},
biburl = {https://dblp.org/rec/conf/iclr/LiuSPGSKS18.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={AIGC}
}
@article{radford2018improving,
title={Improving language understanding by generative pre-training},
author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others},
url = {https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf},
year = {2018},
booktitle = {OpenAI},
keywords = {AIGC, Natural Language Understanding, Pre-Training Techniques},
plm={GPT-1}
}
@article{radfordlanguage,
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},
url = {https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf},
year = {2019},
booktitle = {OpenAI},
plm ={GPT-2},
keywords = {AIGC, Pre-Training Techniques}
}
@inproceedings{DBLP:conf/nips/BrownMRSKDNSSAA20,
author = {Tom B. Brown and
Benjamin Mann and
Nick Ryder and
Melanie Subbiah and
Jared Kaplan and
Prafulla Dhariwal and
Arvind Neelakantan and
Pranav Shyam and
Girish Sastry and
Amanda Askell and
Sandhini Agarwal and
Ariel Herbert{-}Voss and
Gretchen Krueger and
Tom Henighan and
Rewon Child and
Aditya Ramesh and
Daniel M. Ziegler and
Jeffrey Wu and
Clemens Winter and
Christopher Hesse and
Mark Chen and
Eric Sigler and
Mateusz Litwin and
Scott Gray and
Benjamin Chess and
Jack Clark and
Christopher Berner and
Sam McCandlish and
Alec Radford and
Ilya Sutskever and
Dario Amodei},
editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Language Models are Few-Shot Learners},
booktitle = {NeurIPS},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html},
timestamp = {Tue, 19 Jan 2021 15:56:50 +0100},
biburl = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Pre-Training Techniques, AIGC},
plm={GPT-3}
}
@article{DBLP:journals:corr:abs-2301-00234,
author = {Qingxiu Dong and
Lei Li and
Damai Dai and
Ce Zheng and
Zhiyong Wu and
Baobao Chang and
Xu Sun and
Jingjing Xu and
Lei Li and
Zhifang Sui},
title = {A Survey for In-context Learning},
journal = {CoRR},
volume = {abs/2301.00234},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2301.00234},
doi = {10.48550/arXiv.2301.00234},
eprinttype = {arXiv},
eprint = {2301.00234},
timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2301-00234.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={In-Context Learning, Survey}
}
@String(DBLP:journals:corr:abs-2301-00234="This paper surveys and summarizes the progress and challenges of ICL, including ICL's formal definition, correlation to related studies, advanced techniques (training strategies, related analysis) and potential directions.")
@inproceedings{DBLP:conf:naacl:ChenDPMISK22,
author = {Mingda Chen and
Jingfei Du and
Ramakanth Pasunuru and
Todor Mihaylov and
Srini Iyer and
Veselin Stoyanov and
Zornitsa Kozareva},
editor = {Marine Carpuat and
Marie{-}Catherine de Marneffe and
Iv{\'{a}}n Vladimir Meza Ru{\'{\i}}z},
title = {Improving In-Context Few-Shot Learning via Self-Supervised Training},
booktitle = {NAACL},
pages = {3558--3573},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.naacl-main.260},
doi = {10.18653/v1/2022.naacl-main.260},
timestamp = {Mon, 01 Aug 2022 16:27:58 +0200},
biburl = {https://dblp.org/rec/conf/naacl/ChenDPMISK22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={In-Context Learning},
plm={MoE}
}
@string(DBLP:conf:naacl:ChenDPMISK22="This paper proposes to use self-supervision (MLM, NSP, CL, etc.) between pre-training and downstream usage to teach the LM to perform in-context learning. Analysis reveals that:
(1) benefits of self-supervised depends on the amount of training data,
(2) semantic similarity between training and evaluation tasks matters,
(3) adding training objectives without diversity does not help,
(4) model performance improves when choosing similar templates for both self-supervised and downstream tasks,
(5) self-supervised tasks and human-annotated datasets are complementary,
(6) self-supervised-trained models are better at following task instructions.")
@inproceedings{DBLP:conf/iclr/WeiBZGYLDDL22,
author = {Jason Wei and
Maarten Bosma and
Vincent Y. Zhao and
Kelvin Guu and
Adams Wei Yu and
Brian Lester and
Nan Du and
Andrew M. Dai and
Quoc V. Le},
title = {Finetuned Language Models are Zero-Shot Learners},
booktitle = {ICLR},
publisher = {OpenReview.net},
year = {2022},
url = {https://openreview.net/forum?id=gEZrGCozdqR},
timestamp = {Sat, 20 Aug 2022 01:15:42 +0200},
biburl = {https://dblp.org/rec/conf/iclr/WeiBZGYLDDL22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Instruction Tuning}
}
@article{DBLP:journals/corr/abs-2201-08239,
author = {Romal Thoppilan and
Daniel De Freitas and
Jamie Hall and
Noam Shazeer and
Apoorv Kulshreshtha and
Heng{-}Tze Cheng and
Alicia Jin and
Taylor Bos and
Leslie Baker and
Yu Du and
YaGuang Li and
Hongrae Lee and
Huaixiu Steven Zheng and
Amin Ghafouri and
Marcelo Menegali and
Yanping Huang and
Maxim Krikun and
Dmitry Lepikhin and
James Qin and
Dehao Chen and
Yuanzhong Xu and
Zhifeng Chen and
Adam Roberts and
Maarten Bosma and
Yanqi Zhou and
Chung{-}Ching Chang and
Igor Krivokon and
Will Rusch and
Marc Pickett and
Kathleen S. Meier{-}Hellstern and
Meredith Ringel Morris and
Tulsee Doshi and
Renelito Delos Santos and
Toju Duke and
Johnny Soraker and
Ben Zevenbergen and
Vinodkumar Prabhakaran and
Mark Diaz and
Ben Hutchinson and
Kristen Olson and
Alejandra Molina and
Erin Hoffman{-}John and
Josh Lee and
Lora Aroyo and
Ravi Rajakumar and
Alena Butryna and
Matthew Lamm and
Viktoriya Kuzmina and
Joe Fenton and
Aaron Cohen and
Rachel Bernstein and
Ray Kurzweil and
Blaise Aguera{-}Arcas and
Claire Cui and
Marian Croak and
Ed H. Chi and
Quoc Le},
title = {LaMDA: Language Models for Dialog Applications},
journal = {CoRR},
volume = {abs/2201.08239},
year = {2022},
url = {https://arxiv.org/abs/2201.08239},
eprinttype = {arXiv},
eprint = {2201.08239},
timestamp = {Fri, 22 Apr 2022 16:06:31 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-08239.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Instruction Tuning}
}
@article{DBLP:journals/corr/abs-2210-11416,
author = {Hyung Won Chung and
Le Hou and
Shayne Longpre and
Barret Zoph and
Yi Tay and
William Fedus and
Eric Li and
Xuezhi Wang and
Mostafa Dehghani and
Siddhartha Brahma and
Albert Webson and
Shixiang Shane Gu and
Zhuyun Dai and
Mirac Suzgun and
Xinyun Chen and
Aakanksha Chowdhery and
Sharan Narang and
Gaurav Mishra and
Adams Yu and
Vincent Y. Zhao and
Yanping Huang and
Andrew M. Dai and
Hongkun Yu and
Slav Petrov and
Ed H. Chi and
Jeff Dean and
Jacob Devlin and
Adam Roberts and
Denny Zhou and
Quoc V. Le and
Jason Wei},
title = {Scaling Instruction-Finetuned Language Models},
journal = {CoRR},
volume = {abs/2210.11416},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2210.11416},
doi = {10.48550/arXiv.2210.11416},
eprinttype = {arXiv},
eprint = {2210.11416},
timestamp = {Wed, 26 Oct 2022 08:16:51 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2210-11416.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Instruction Tuning}
}
@inproceedings{DBLP:conf/emnlp/WangMAKMNADASPK22,
author = {Yizhong Wang and
Swaroop Mishra and
Pegah Alipoormolabashi and
Yeganeh Kordi and
Amirreza Mirzaei and
Atharva Naik and
Arjun Ashok and
Arut Selvan Dhanasekaran and
Anjana Arunkumar and
David Stap and
Eshaan Pathak and
Giannis Karamanolakis and
Haizhi Gary Lai and
Ishan Purohit and
Ishani Mondal and
Jacob Anderson and
Kirby Kuznia and
Krima Doshi and
Kuntal Kumar Pal and
Maitreya Patel and
Mehrad Moradshahi and
Mihir Parmar and
Mirali Purohit and
Neeraj Varshney and
Phani Rohitha Kaza and
Pulkit Verma and
Ravsehaj Singh Puri and
Rushang Karia and
Savan Doshi and
Shailaja Keyur Sampat and
Siddhartha Mishra and
Sujan Reddy A and
Sumanta Patro and
Tanay Dixit and
Xudong Shen},
editor = {Yoav Goldberg and
Zornitsa Kozareva and
Yue Zhang},
title = {Super-NaturalInstructions: Generalization via Declarative Instructions
on 1600+ {NLP} Tasks},
booktitle = {EMNLP},
pages = {5085--5109},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://aclanthology.org/2022.emnlp-main.340},
timestamp = {Tue, 07 Feb 2023 17:10:51 +0100},
biburl = {https://dblp.org/rec/conf/emnlp/WangMAKMNADASPK22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Instruction Tuning}
}
@article{DBLP:journals:corr:abs-2205-10782,
author = {Or Honovich and
Uri Shaham and
Samuel R. Bowman and
Omer Levy},
title = {Instruction Induction: From Few Examples to Natural Language Task
Descriptions},
journal = {CoRR},
volume = {abs/2205.10782},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2205.10782},
doi = {10.48550/arXiv.2205.10782},
eprinttype = {arXiv},
eprint = {2205.10782},
timestamp = {Mon, 09 Jan 2023 08:11:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2205-10782.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={In-Context Learning, Chain of Thought},
}
@String(DBLP:journals:corr:abs-2205-10782="(1) 探索了利用LLM在几个样本的情况下归纳出任务指令的能力；
(2) 测量两个指标：1. 模型归纳指令与人类归纳的指令对比，2. 利用模型归纳的指令作为prompt进行预测的执行准确率；
(3) 相比于GPT-3，InstructGPT效果更好，理所当然。")
@article{DBLP:journals/corr/abs-2211-01910,
author = {Yongchao Zhou and
Andrei Ioan Muresanu and
Ziwen Han and
Keiran Paster and
Silviu Pitis and
Harris Chan and
Jimmy Ba},
title = {Large Language Models Are Human-Level Prompt Engineers},
journal = {CoRR},
volume = {abs/2211.01910},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2211.01910},
doi = {10.48550/arXiv.2211.01910},
eprinttype = {arXiv},
eprint = {2211.01910},
timestamp = {Fri, 04 Nov 2022 13:48:49 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2211-01910.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Prompt Engineering}
}
@article{DBLP:journals/corr/abs-2212-10560,
author = {Yizhong Wang and
Yeganeh Kordi and
Swaroop Mishra and
Alisa Liu and
Noah A. Smith and
Daniel Khashabi and
Hannaneh Hajishirzi},
title = {Self-Instruct: Aligning Language Model with Self Generated Instructions},
journal = {CoRR},
volume = {abs/2212.10560},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2212.10560},
doi = {10.48550/arXiv.2212.10560},
eprinttype = {arXiv},
eprint = {2212.10560},
timestamp = {Wed, 04 Jan 2023 16:01:37 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2212-10560.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords={Instruction Tuning}