Fetching the paper…
Reading the bibliography…
Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi · 2016
Earlier work this paper cites.
A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen · 2016
Earlier work this paper cites.
The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández · 2016
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, and Richard Socher · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Mask-predict: Parallel decoding of conditional masked language models
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer · 2019
Earlier work this paper cites.
Openwebtext corpus
Aaron Gokaslan and Vanya Cohen · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Fine-tuning by curriculum learning for non-autoregressive neural machine translation
Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
Earlier work this paper cites.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Earlier work this paper cites.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Earlier work this paper cites.
Lower perplexity is not always human-like
Tatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida, Masayuki Asahara, and Kentaro Inui · 2021
Earlier work this paper cites.
Limitations of autoregressive models and their alternatives
Chu-Cheng Lin, Aaron Jaech, Xin Li, Matthew R. Gormley, and Jason Eisner · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Winogrande: an adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
Cited alongside, same era.
Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman · 2022
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with IO-awareness
Tri Dao, Daniel Y Fu, Stefano Ermon, Atri Rudra, and Christopher Re · 2022
LongloRA: Efficient fine-tuning of long-context large language models
Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia · 2024
Closest in time.
FlashAttention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2024
Closest in time.
Stream of search (sos): Learning to search in language
Kanishk Gandhi, Denise H J Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, and Noah Goodman · 2024
Closest in time.
A framework for few-shot language model evaluation, 07 2024
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2024
Closest in time.
Better & faster large language models via multi-token prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, David Lopez-Paz, and Gabriel Synnaeve · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et al · 2022
Cited alongside, same era.
Autoregressive diffusion models
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Diffusion-lm improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto · 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
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
Olmes: A standard for language model evaluations, 2024
Yuling Gu, Oyvind Tafjord, Bailey Kuehl, Dany Haddad, Jesse Dodge, and Hannaneh Hajishirzi · 2024
Closest in time.
Efficient continual pre-training by mitigating the stability gap, 2024
Yiduo Guo, Jie Fu, Huishuai Zhang, Dongyan Zhao, and Yikang Shen · 2024
Closest in time.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack W. Rae, and Laurent Sifre · 2024
Closest in time.
Amortizing intractable inference in large language models
Edward Hu*, Moksh Jain*, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, and Nikolay Malkin · 2024
Closest in time.
Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou · 2024
Closest in time.
Cllms: Consistency large language models
Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, and Hao Zhang · 2024
Closest in time.
Autoregressive image generation without vector quantization
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He · 2024
Closest in time.
Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2024
Closest in time.
Your absorbing discrete diffusion secretly models the conditional distributions of clean data
Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li · 2024
Closest in time.
The fineweb datasets: Decanting the web for the finest text data at scale, 2024
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf · 2024
Closest in time.
Simple and effective masked diffusion language models
Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, and Volodymyr Kuleshov · 2024
Closest in time.
Scaling smart: Accelerating large language model pre-training with small model initialization
Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, and Mehrdad Farajtabar · 2024
Closest in time.
Flashattention-3: Fast and accurate attention with asynchrony and low-precision
Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao · 2024
Closest in time.
Simplified and generalized masked diffusion for discrete data
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias · 2024
Closest in time.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Closest in time.
Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, et al · 2024
Closest in time.
The mamba in the llama: Distilling and accelerating hybrid models
Junxiong Wang, Daniele Paliotta, Avner May, Alexander M Rush, and Tri Dao · 2024
Closest in time.
Do language models plan ahead for future tokens?
Wilson Wu, John Xavier Morris, and Lionel Levine · 2024
Closest in time.
Travelplanner: A benchmark for real-world planning with language agents
Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su · 2024
Closest in time.
Lemur: Harmonizing natural language and code for language agents
Yiheng Xu, Hongjin SU, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, and Tao Yu · 2024
Closest in time.
Improving and unifying discrete&continuous-time discrete denoising diffusion
Lingxiao Zhao, Xueying Ding, Lijun Yu, and Leman Akoglu · 2024
Closest in time.
A reparameterized discrete diffusion model for text generation
Lin Zheng, Jianbo Yuan, Lei Yu, and Lingpeng Kong · 2024
Closest in time.
AGIEval: A human-centric benchmark for evaluating foundation models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan · 2024
Closest in time.