Improving Open-Ended Text Generation via Adaptive Decoding

A corpus and cloze evaluation for deeper understanding of commonsense stories

Mostafazadeh, N., Chambers, N., He, X., Parikh, D., Batra, D., Vanderwende, L., Kohli, P., and Allen, J · 2016

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Pointer sentinel mixture models

Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2017

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Hierarchical neural story generation

Fan, A., Lewis, M., and Dauphin, Y · 2018

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Language models are unsupervised multitask learners

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019

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Language models are few-shot learners

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020

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The curious case of neural text degeneration

Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020

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Don’t say that! making inconsistent dialogue unlikely with unlikelihood training

Li, M., Roller, S., Kulikov, I., Welleck, S., Boureau, Y.-L., Cho, K., and Weston, J · 2020

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Beyond accuracy: Behavioral testing of NLP models with CheckList

Ribeiro, M. T., Wu, T., Guestrin, C., and Singh, S · 2020

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Neural text generation with unlikelihood training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J · 2020

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Mirostat: a neural text decoding algorithm that directly controls perplexity

Basu, S., Ramachandran, G. S., Keskar, N. S., and Varshney, L. R · 2021

Cited alongside, same era.

Training verifiers to solve math word problems, 2021

Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021

Cited alongside, same era.

SimCSE: Simple contrastive learning of sentence embeddings

Gao, T., Yao, X., and Chen, D · 2021

Cited alongside, same era.

Mauve: Measuring the gap between neural text and human text using divergence frontiers

Pillutla, K., Swayamdipta, S., Zellers, R., Thickstun, J., Welleck, S., Choi, Y., and Harchaoui, Z · 2021

Cited alongside, same era.

Truncation sampling as language model desmoothing

Hewitt, J., Manning, C., and Liang, P · 2022

Cited alongside, same era.

A contrastive framework for neural text generation

On the efficacy of sampling adapters

Meister, C., Pimentel, T., Malagutti, L., Wilcox, E., and Cotterell, R · 2023

Later among the works it cites.

Gpt-4 technical report, 2023

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