Fetching the paper…
Reading the bibliography…
Large models have demonstrated significant progress across various domains, particularly in tasks related to text generation.
Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
C.-Y. Lin · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
S. Banerjee and A. Lavie · 2005
Earlier work this paper cites.
Compositional semantic parsing on semi-structured tables
P. Pasupat and P. Liang · 2015
Earlier work this paper cites.
A survey and experimental comparison of distributed sparql engines for very large rdf data
I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2018
Earlier work this paper cites.
A call for clarity in reporting bleu scores
M. Post · 2018
Earlier work this paper cites.
The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2019
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
Tapas: Weakly supervised table parsing via pre-training
J. Herzig, P. K. Nowak, T. Mueller, F. Piccinno, and J. Eisenschlos · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
Earlier work this paper cites.
Tabert: Pretraining for joint understanding of textual and tabular data
P. Yin, G. Neubig, W.-t. Yih, and S. Riedel · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
Earlier work this paper cites.
Tapex: Table pre-training via learning a neural sql executor
Q. Liu, B. Chen, J. Guo, M. Ziyadi, Z. Lin, W. Chen, and J.-G. Lou · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
V. Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, A. Raja, M. Dey, et al · 2021
Cited alongside, same era.
Binding language models in symbolic languages
Z. Cheng, T. Xie, P. Shi, C. Li, R. Nadkarni, Y. Hu, C. Xiong, D. Radev, M. Ostendorf, L. Zettlemoyer, et al · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma, et al · 2022
Cited alongside, same era.
Turl: Table understanding through representation learning
X. Deng, H. Sun, A. Lees, Y. Wu, and C. Yu · 2022
Bloom: A 176b-parameter open-access multilingual language model
B. Workshop, T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, et al · 2022
Later among the works it cites.
Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
T. Xie, C. H. Wu, P. Shi, R. Zhong, T. Scholak, M. Yasunaga, C.-S. Wu, M. Zhong, P. Yin, S. I. Wang, et al · 2022
Later among the works it cites.
Large language models are few (1)-shot table reasoners
W. Chen · 2023
Closest in time.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, et al · 2023
Closest in time.
Qlora: Efficient finetuning of quantized llms
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Glm: General language model pretraining with autoregressive blank infilling
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang · 2022
Cited alongside, same era.
Pasta: Table-operations aware fact verification via sentence-table cloze pre-training
Z. Gu, J. Fan, N. Tang, P. Nakov, X. Zhao, and X. Du · 2022
Cited alongside, same era.
S2sql: Injecting syntax to question-schema interaction graph encoder for text-to-sql parsers
B. Hui, R. Geng, L. Wang, B. Qin, Y. Li, B. Li, J. Sun, and Y. Li · 2022
Cited alongside, same era.
Omnitab: Pretraining with natural and synthetic data for few-shot table-based question answering
Z. Jiang, Y. Mao, P. He, G. Neubig, and W. Chen · 2022
Cited alongside, same era.
Plog: Table-to-logic pretraining for logical table-to-text generation
A. Liu, H. Dong, N. Okazaki, S. Han, and D. Zhang · 2022
Cited alongside, same era.
Cross-task generalization via natural language crowdsourcing instructions
S. Mishra, D. Khashabi, C. Baral, and H. Hajishirzi · 2022
Cited alongside, same era.
Fetaqa: Free-form table question answering
L. Nan, C. Hsieh, Z. Mao, X. V. Lin, N. Verma, R. Zhang, W. Kryściński, H. Schoelkopf, R. Kong, X. Tang, et al · 2022
Cited alongside, same era.
Closest in time.
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al · 2023
Closest in time.
OpenAI · 2023
Closest in time.
G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, and J. Launay · 2023
Closest in time.
B. Peng, C. Li, P. He, M. Galley, and J. Gao · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model, 2023
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
Closest in time.
Y. Ye, B. Hui, M. Yang, B. Li, F. Huang, and Y. Li · 2023
Closest in time.
Tablegpt: Towards unifying tables, nature language and commands into one gpt
L. Zha, J. Zhou, L. Li, R. Wang, Q. Huang, S. Yang, J. Yuan, C. Su, X. Li, A. Su, et al · 2023
Closest in time.
Qtsumm: A new benchmark for query-focused table summarization
Y. Zhao, Z. Qi, L. Nan, B. Mi, Y. Liu, W. Zou, S. Han, X. Tang, Y. Xu, A. Cohan, et al · 2023
Closest in time.
Scaling instruction-finetuned language models
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma, et al · 2024
Closest in time.