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The curious case of neural text degeneration
Original
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Neural text generation with unlikelihood training
Original
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
Earlier work this paper cites.
Findings of the 2014 workshop on statistical machine translation
Ondřej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, et al. 2014 · 2014
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Original
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015 · 2015
Earlier work this paper cites.
Discriminative reranking for grammatical error correction with statistical machine translation
Tomoya Mizumoto and Yuji Matsumoto. 2016 · 2016
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Original
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright. 2019 · 2019
Earlier work this paper cites.
Program synthesis with large language models
Original
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021
Earlier work this paper cites.

Similar

Evaluating large language models trained on code
Original
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
Cited alongside, same era.
Simcls: A simple framework for contrastive learning of abstractive summarization
Original
Yixin Liu and Pengfei Liu. 2021 · 2021
Cited alongside, same era.
Codet: Code generation with generated tests
Original
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Large language models are reasoning teachers
Original
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
Cited alongside, same era.
Competition-level code generation with alphacode

Then

Summareranker: A multi-task mixture-of-experts re-ranking framework for abstractive summarization
Original
Mathieu Ravaut, Shafiq Joty, and Nancy F Chen. 2022 · 2022
Later among the works it cites.
Natural language to code translation with execution
Original
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang. 2022 · 2022
Later among the works it cites.
Solving math word problems with process-and outcome-based feedback
Original
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022
Later among the works it cites.
Self-consistency improves chain of thought reasoning in language models
Original
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
Later among the works it cites.

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2023

Lightweight reranking for language model generations

Jain, Siddhartha, Ma, Xiaofei, Deoras, Anoop et al.

Understand

Large Language Models (LLMs) can exhibit considerable variation in the quality of their sampled outputs.

Reranking and selecting the best generation from the sampled set is a popular way of obtaining strong gains in generation quality.
In this paper, we present a novel approach for reranking LLM generations.
Unlike other techniques that might involve additional inferences or training a specialized reranker, our approach relies on easy to compute pairwise statistics between the generations that have minimal compute overhead.

Built on

The curious case of neural text degeneration
Original
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Neural text generation with unlikelihood training
Original
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
Earlier work this paper cites.
Findings of the 2014 workshop on statistical machine translation
Ondřej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, et al. 2014 · 2014
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Original
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015 · 2015
Earlier work this paper cites.
Discriminative reranking for grammatical error correction with statistical machine translation
Tomoya Mizumoto and Yuji Matsumoto. 2016 · 2016
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Original
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright. 2019 · 2019
Earlier work this paper cites.
Program synthesis with large language models
Original
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021
Earlier work this paper cites.

Similar

Evaluating large language models trained on code
Original
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
Cited alongside, same era.
Simcls: A simple framework for contrastive learning of abstractive summarization
Original
Yixin Liu and Pengfei Liu. 2021 · 2021
Cited alongside, same era.
Codet: Code generation with generated tests
Original
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Large language models are reasoning teachers
Original
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
Cited alongside, same era.
Competition-level code generation with alphacode

Then

Summareranker: A multi-task mixture-of-experts re-ranking framework for abstractive summarization
Original
Mathieu Ravaut, Shafiq Joty, and Nancy F Chen. 2022 · 2022
Later among the works it cites.
Natural language to code translation with execution
Original
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang. 2022 · 2022
Later among the works it cites.
Solving math word problems with process-and outcome-based feedback
Original
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022
Later among the works it cites.
Self-consistency improves chain of thought reasoning in language models
Original
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022 · 2022

Cited alongside, same era.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022

Cited alongside, same era.

Formal mathematics statement curriculum learning

Original

Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, and Ilya Sutskever. 2022 · 2022

Cited alongside, same era.

Draft, sketch, and prove: Guiding formal theorem provers with informal proofs

Original

Albert Q Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, and Guillaume Lample. 2022a

Cited in the paper.

Pairreranker: Pairwise reranking for natural language generation

Original

Dongfu Jiang, Bill Yuchen Lin, and Xiang Ren. 2022b

Cited in the paper.

Autoformalization with large language models

Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy. 2022 · 2022

Later among the works it cites.

Coder reviewer reranking for code generation

Original

Tianyi Zhang, Tao Yu, Tatsunori B Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, and Sida I Wang. 2022 · 2022

Later among the works it cites.

Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes

Original

Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023

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