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Compositional generalization is an important ability of language models and has many different manifestations.
Measures of distance between probability distributions
J.K Chung, P.L Kannappan, C.T Ng, and P.K Sahoo. 1989 · 1989
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The kendall rank correlation coefficient
Hervé Abdi. 2007 · 2007
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Investigating pretrained language models for graph-to-text generation
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, and Iryna Gurevych. 2020 · 2007
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Adam: A method for stochastic optimization
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The webnlg challenge: Generating text from RDF data
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The E2E dataset: New challenges for end-to-end generation
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Challenges in data-to-document generation
Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. 2017 · 2017
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Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer. 2018 · 2018
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End-to-end content and plan selection for data-to-text generation
Sebastian Gehrmann, Falcon Z. Dai, Henry Elder, and Alexander M. Rush. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden M. Lake and Marco Baroni. 2018 · 2018
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Handling divergent reference texts when evaluating table-to-text generation
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Semantic noise matters for neural natural language generation
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Language models are unsupervised multitask learners
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The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)
Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van Der Lee, Simon Mille, Diego Moussallem, and Anastasia Shimorina. 2020 · 2020
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Compositionality decomposed: How do neural networks generalise? (extended abstract)
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni. 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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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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State-of-the-art generalisation research in NLP: a taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, and Zhijing Jin. 2022 · 2022
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Text-to-text pre-training for data-to-text tasks
Mihir Kale and Abhinav Rastogi. 2020 · 2020
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Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020 · 2020
Cited alongside, same era.
COGS: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen. 2020 · 2020
Cited alongside, same era.
BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Improving compositional generalization with self-training for data-to-text generation
Sanket Vaibhav Mehta, Jinfeng Rao, Yi Tay, Mihir Kale, Ankur Parikh, and Emma Strubell. 2022 · 2022
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Making transformers solve compositional tasks
Santiago Ontañón, Joshua Ainslie, Zachary Fisher, and Vaclav Cvicek. 2022 · 2022
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Large language models are not fair evaluators
Peiyi Wang, Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023 · 2023
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