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Data plays a fundamental role in training Large Language Models (LLMs).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 1906
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
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Using the sir algorithm to simulate posterior distributions
D.B. Rubin. 1988 · 1987
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On the resemblance and containment of documents
Andrei Z Broder. 1997 · 1997
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
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Mtld, vocd-d, and hd-d: A validation study of sophisticated approaches to lexical diversity assessment
Philip M McCarthy and Scott Jarvis. 2010 · 2010
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Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. 2020 · 2010
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Efficient k-nearest neighbor graph construction for generic similarity measures
Wei Dong, Charikar Moses, and Kai Li. 2011 · 2011
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Distributionally robust language modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B Hashimoto, and Percy Liang. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 2019
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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Overview and importance of data quality for machine learning tasks
Abhinav Jain, Hima Patel, Lokesh Nagalapatti, Nitin Gupta, Sameep Mehta, Shanmukha Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, and Vitobha Munigala. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Distributionally robust neural networks
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020 · 2020
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Economic hyperparameter optimization with blended search strategy
Chi Wang, Qingyun Wu, Silu Huang, and Amin Saied. 2020 · 2020
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Ccnet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Édouard Grave. 2020 · 2020
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021 · 2021
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An empirical exploration in quality filtering of text data
Leo Gao. 2021 · 2021
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Data quality for machine learning tasks
Nitin Gupta, Shashank Mujumdar, Hima Patel, Satoshi Masuda, Naveen Panwar, Sambaran Bandyopadhyay, Sameep Mehta, Shanmukha Guttula, Shazia Afzal, Ruhi Sharma Mittal, et al. 2021 · 2021
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What’s in the box? a preliminary analysis of undesirable content in the common crawl corpus
Alexandra Sasha Luccioni and Joseph D Viviano. 2021 · 2021
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. 2021 · 2021
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. 2021 · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Challenges in detoxifying language models
Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, and Po-Sen Huang. 2021 · 2021
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Detoxifying language models risks marginalizing minority voices
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, and Dan Klein. 2021 · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al. 2022 · 2022
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On the origin of hallucinations in conversational models: Is it the datasets or the models?
Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022 · 2022
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Bert on a data diet: Finding important examples by gradient-based pruning
Mohsen Fayyaz, Ehsan Aghazadeh, Ali Modarressi, Mohammad Taher Pilehvar, Yadollah Yaghoobzadeh, and Samira Ebrahimi Kahou. 2022 · 2022
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Analyzing and addressing the difference in toxicity prediction between different comments with same semantic meaning in google’s perspective api
SK Gargee, Pranav Bhargav Gopinath, Shridhar Reddy SR Kancharla, CR Anand, and Anoop S Babu. 2022 · 2022
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
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Whose language counts as high quality? measuring language ideologies in text data selection
Suchin Gururangan, Dallas Card, Sarah Dreier, Emily Gade, Leroy Wang, Zeyu Wang, Luke Zettlemoyer, and Noah A. Smith. 2022 · 2022
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Scaling laws and interpretability of learning from repeated data
Danny Hernandez, Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, et al. 2022 · 2022
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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022 · 2022
Cited alongside, same era.
Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, et al. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
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Can large language models understand real-world complex instructions?
Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Lida Chen, Xintao Wang, Yuncheng Huang, et al. 2023 · 2023
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Data-efficient finetuning using cross-task nearest neighbors
Hamish Ivison, Noah A. Smith, Hannaneh Hajishirzi, and Pradeep Dasigi. 2023 · 2023
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Exploring the benefits of training expert language models over instruction tuning
Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. 2023 · 2023
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Phi-2: The surprising power of small language models
Mojan Javaheripi and Sébastien Bubeck. 2023 · 2023
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Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, and Xiangang Li. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, and Yejin Choi. 2022 · 2022
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Quality at a glance: An audit of web-crawled multilingual datasets
Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, and Mofetoluwa Adeyemi. 2022 · 2022
Cited alongside, same era.
Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2021 · 2022
Cited alongside, same era.
A new generation of perspective api: Efficient multilingual character-level transformers
Alyssa Lees, Vinh Q Tran, Yi Tay, Jeffrey Sorensen, Jai Gupta, Donald Metzler, and Lucy Vasserman. 2022 · 2022
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How pre-trained language models capture factual knowledge? a causal-inspired analysis
Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Cheng-Jie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, and Qun Liu. 2022 · 2022
Cited alongside, same era.
An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
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Brando Miranda, Patrick Yu, Yu-Xiong Wang, and Sanmi Koyejo. 2022 · 2022
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Reframing instructional prompts to GPTk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2022 · 2022
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Followbench: A multi-level fine-grained constraints following benchmark for large language models
Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, and Wei Wang. 2023 · 2023
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The minipile challenge for data-efficient language models
Jean Kaddour. 2023 · 2023
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Openassistant conversations–democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, et al. 2023 · 2023
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Do models really learn to follow instructions? an empirical study of instruction tuning
Po-Nien Kung and Nanyun Peng. 2023 · 2023
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Exploring format consistency for instruction tuning
Shihao Liang, Kunlun Zhu, Runchu Tian, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, and Maosong Sun. 2023 · 2023
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Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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D2 pruning: Message passing for balancing diversity and difficulty in data pruning
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When less is more: Investigating data pruning for pretraining llms at scale
Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, and Sara Hooker. 2023 · 2023
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Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. 2023 · 2023
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Scaling data-constrained language models
Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. 2023 · 2023
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Orca: Progressive learning from complex explanation traces of gpt-4
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Culturax: A cleaned, enormous, and multilingual dataset for large language models in 167 languages
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Codegen2: Lessons for training llms on programming and natural languages
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OpenAI. 2023 · 2023
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The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only
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Slimpajama-dc: Understanding data combinations for llm training
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An empirical study of instruction-tuning large language models in chinese
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Dynamics of instruction tuning: Each ability of large language models has its own growth pace
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Stanford alpaca: An instruction-following llama model
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D4: Improving llm pretraining via document de-duplication and diversification
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Explore-instruct: Enhancing domain-specific instruction coverage through active exploration
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Self-evolved diverse data sampling for efficient instruction tuning
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To repeat or not to repeat: Insights from scaling llm under token-crisis
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Scaling relationship on learning mathematical reasoning with large language models
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Mammoth: Building math generalist models through hybrid instruction tuning
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Data-centric artificial intelligence: A survey
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Towards a unified multi-dimensional evaluator for text generation
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