Fetching the paper…
Reading the bibliography…
Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B. Dolan. 2015 · 2015
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Earlier work this paper cites.
Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
Earlier work this paper cites.
Multi-fact correction in abstractive text summarization
Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu. 2020 · 2020
Earlier work this paper cites.
Improved natural language generation via loss truncation
Daniel Kang and Tatsunori B. Hashimoto. 2020 · 2020
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
Earlier work this paper cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Earlier work this paper cites.
Cliff: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
Cited alongside, same era.
Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
Cited alongside, same era.
Dexperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
Cited alongside, same era.
Entity-based knowledge conflicts in question answering
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Coherence boosting: When your pretrained language model is not paying enough attention
Nikolay Malkin, Zhen Wang, and Nebojsa Jojic. 2021 · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans. 2022 · 2022
Later among the works it cites.
Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
Later among the works it cites.
Nonparametric masked language modeling
Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen tau Yih, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improving factual consistency of abstractive summarization via question answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, and Bing Xiang. 2021 · 2021
Cited alongside, same era.
Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
Cited alongside, same era.
Enhancing factual consistency of abstractive summarization
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2021 · 2021
Cited alongside, same era.
Data distributional properties drive emergent in-context learning in transformers
Stephanie C. Y. Chan, Adam Santoro, Andrew Kyle Lampinen, Jane X. Wang, Aaditya K Singh, Pierre H. Richemond, Jay Mcclelland, and Felix Hill. 2022 · 2022
Cited alongside, same era.
Rich knowledge sources bring complex knowledge conflicts: Recalibrating models to reflect conflicting evidence
Hung-Ting Chen, Michael Zhang, and Eunsol Choi. 2022 · 2022
Cited alongside, same era.
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, et al. 2022 · 2022
Cited alongside, same era.
Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, and Yulia Tsvetkov. 2023 · 2023
Closest in time.
The memotrap dataset
Alisa Liu and Jiacheng Liu. 2023 · 2023
Closest in time.
Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023 · 2023
Closest in time.
LLaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Closest in time.
Retrieval-augmented multimodal language modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023
Closest in time.
Benchmarking large language models for news summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B. Hashimoto. 2023 · 2023
Closest in time.
Context-faithful prompting for large language models
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023 · 2023
Closest in time.