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Diffusion-based large language models (DLLMs) have shown promise for non-autoregressive text generation, but their deployment is constrained by large model sizes and heavy computational costs.
Hellaswag: Can a machine really finish your sentence?
Zellers, R.; Holtzman, A.; Bisk, Y.; Farhadi, A.; and Choi, Y. 2019 · 1905
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Optimal brain damage
LeCun, Y.; Denker, J.; and Solla, S. 1989 · 1989
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Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
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Evaluating Large Language Models Trained on Code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; de Oliveira Pinto, H. P.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; Ray, A.; Puri, R.; Krueger, G.; Petrov, M.; Khlaaf, H.; Sastry, G.; Mishkin, P.; Chan, B.; Gray, S.; Ryder, N.; Pavlov, M.; Power, A.; Kaiser, L.; Bavarian, M.; Winter, C.; Tillet, P.; Such, F. P.; Cummings, D.; Plappert, M.; Chantzis, F.; Barnes, E.; Herbert-Voss, A.; Guss, W. H.; Nichol, A.; Paino, A.; Tezak, N.; Tang, J.; Babuschkin, I.; Balaji, S.; Jain, S.; Saunders, W.; Hesse, C.; Carr, A. N.; Leike, J.; Achiam, J.; Misra, V.; Morikawa, E.; Radford, A.; Knight, M.; Brundage, M.; Murati, M.; Mayer, K.; Welinder, P.; McGrew, B.; Amodei, D.; McCandlish, S.; Sutskever, I.; and Zaremba, W. 2021 · 2021
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Training Verifiers to Solve Math Word Problems
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 · 2021
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Measuring Massive Multitask Language Understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2021 · 2021
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TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S.; Hilton, J.; and Evans, O. 2021 · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K.; Bras, R. L.; Bhagavatula, C.; and Choi, Y. 2021 · 2021
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T.; Fu, D.; Ermon, S.; Rudra, A.; and Ré, C. 2022 · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
Fedus, W.; Zoph, B.; and Shazeer, N. 2022 · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Frantar, E.; Ashkboos, S.; Hoefler, T.; and Alistarh, D. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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Bai, J.; Bai, S.; Chu, Y.; Cui, Z.; Dang, K.; Deng, X.; Fan, Y.; Ge, W.; Han, Y.; Huang, F.; et al. 2023 · 2023
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The Reversal Curse: LLMs trained on” A is B” fail to learn” B is A”
Berglund, L.; Tong, M.; Kaufmann, M.; Balesni, M.; Stickland, A. C.; Korbak, T.; and Evans, O. 2023 · 2023
Cited alongside, same era.
Diffusion models in vision: A survey
Croitoru, F.-A.; Hondru, V.; Ionescu, R. T.; and Shah, M. 2023 · 2023
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C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
Huang, Y.; Bai, Y.; Zhu, Z.; Zhang, J.; Zhang, J.; Su, T.; Liu, J.; Lv, C.; Zhang, Y.; Lei, J.; Fu, Y.; Sun, M.; and He, J. 2023 · 2023
The llama 3 herd of models
Dubey, A.; Jauhri, A.; Pandey, A.; Kadian, A.; Al-Dahle, A.; Letman, A.; Mathur, A.; Schelten, A.; Yang, A.; Fan, A.; et al. 2024 · 2024
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Scaling diffusion language models via adaptation from autoregressive models
Gong, S.; Agarwal, S.; Zhang, Y.; Ye, J.; Zheng, L.; Li, M.; An, C.; Zhao, P.; Bi, W.; Han, J.; et al. 2024 · 2024
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Awq: Activation-aware weight quantization for on-device llm compression and acceleration
Lin, J.; Tang, J.; Tang, H.; Yang, S.; Chen, W.-M.; Wang, W.-C.; Xiao, G.; Dang, X.; Gan, C.; and Han, S. 2024 · 2024
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Gptvq: The blessing of dimensionality for llm quantization
Van Baalen, M.; Kuzmin, A.; Koryakovskiy, I.; Nagel, M.; Couperus, P.; Bastoul, C.; Mahurin, E.; Blankevoort, T.; and Whatmough, P. 2024 · 2024
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ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E.; Seßler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. 2023 · 2023
Cited alongside, same era.
Llm-qat: Data-free quantization aware training for large language models
Liu, Z.; Oguz, B.; Zhao, C.; Chang, E.; Stock, P.; Mehdad, Y.; Shi, Y.; Krishnamoorthi, R.; and Chandra, V. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
Cited alongside, same era.
Smoothquant: Accurate and efficient post-training quantization for large language models
Xiao, G.; Lin, J.; Seznec, M.; Wu, H.; Demouth, J.; and Han, S. 2023 · 2023
Cited alongside, same era.
Quarot: Outlier-free 4-bit inference in rotated llms
Ashkboos, S.; Mohtashami, A.; Croci, M. L.; Li, B.; Cameron, P.; Jaggi, M.; Alistarh, D.; Hoefler, T.; and Hensman, J. 2024 · 2024
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y.; Zellers, R.; Gao, J.; Choi, Y.; et al. 2020a
Cited in the paper.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y.; Zellers, R.; Gao, J.; Choi, Y.; et al. 2020b
Cited in the paper.
Hu, X.; Cheng, Y.; Yang, D.; Xu, Z.; Yuan, Z.; Yu, J.; Xu, C.; Jiang, Z.; and Zhou, S. 2025 · 2025
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LaViDa: A Large Diffusion Language Model for Multimodal Understanding
Li, S.; Kallidromitis, K.; Bansal, H.; Gokul, A.; Kato, Y.; Kozuka, K.; Kuen, J.; Lin, Z.; Chang, K. W.; and Grover, A. 2025 · 2025
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Large language diffusion models
Nie, S.; Zhu, F.; You, Z.; Zhang, X.; Ou, J.; Hu, J.; Zhou, J.; Lin, Y.; Wen, J.-R.; and Li, C. 2025 · 2025
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Dream 7B
Ye, J.; Xie, Z.; Zheng, L.; Gao, J.; Wu, Z.; Jiang, X.; Li, Z.; and Kong, L. 2025 · 2025
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Llada-v: Large language diffusion models with visual instruction tuning
You, Z.; Nie, S.; Zhang, X.; Hu, J.; Zhou, J.; Lu, Z.; Wen, J.-R.; and Li, C. 2025 · 2025
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LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models
Zhu, F.; Wang, R.; Nie, S.; Zhang, X.; Wu, C.; Hu, J.; Zhou, J.; Chen, J.; Lin, Y.; Wen, J.-R.; et al. 2025 · 2025
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