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Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations.
Learning to forget: Continual prediction with lstm
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ANOVA and ANCOVA: a GLM approach
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Efficient estimation of word representations in vector space
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Findings of the 2014 workshop on statistical machine translation
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Glove: Global vectors for word representation
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Towards better decoding and language model integration in sequence to sequence models
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The lambada dataset: Word prediction requiring a broad discourse context
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
W. Ling, D. Yogatama, C. Dyer, and P. Blunsom · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
C. Clark, K. Lee, M.-W. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova · 2019
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Plug and play language models: A simple approach to controlled text generation
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2019
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Ctrl: A conditional transformer language model for controllable generation
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher · 2019
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Language models are unsupervised multitask learners
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Release strategies and the social impacts of language models
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Hellaswag: Can a machine really finish your sentence?
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi · 2019
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Aqua: Asp-based visual question answering
K. Basu, F. Shakerin, and G. Gupta · 2020
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Piqa: Reasoning about physical commonsense in natural language
Y. Bisk, R. Zellers, J. Gao, Y. Choi, et al · 2020
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Language models are few-shot learners
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Compositional visual generation with energy based models
Y. Du, S. Li, and I. Mordatch · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
S. Gehman, S. Gururangan, M. Sap, Y. Choi, and N. A. Smith · 2020
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Gedi: Generative discriminator guided sequence generation
B. Krause, A. D. Gotmare, B. McCann, N. S. Keskar, S. Joty, R. Socher, and N. F. Rajani · 2020
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A general language assistant as a laboratory for alignment
A. Askell, Y. Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann, N. DasSarma, et al · 2021
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Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba · 2021
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Training verifiers to solve math word problems
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Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, C. Hesse, and J. Schulman · 2021
Crosslingual generalization through multitask finetuning
N. Muennighoff, T. Wang, L. Sutawika, A. Roberts, S. R. Biderman, T. L. Scao, M. S. Bari, S. Shen, Z. X. Yong, H. Schoelkopf, X. Tang, D. R. Radev, A. F. Aji, K. Almubarak, S. Albanie, Z. Alyafeai, A. Webson, E. Raff, and C. Raffel · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen · 2022
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Show your work: Scratchpads for intermediate computation with language models
M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, C. Sutton, and A. Odena · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Training language models to follow instructions with human feedback
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Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
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A theoretical analysis of the repetition problem in text generation
Z. Fu, W. Lam, A. M.-C. So, and B. Shi · 2021
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Stylegan-nada: Clip-guided domain adaptation of image generators
R. Gal, O. Patashnik, H. Maron, G. Chechik, and D. Cohen-Or · 2021
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A framework for few-shot language model evaluation, Sept. 2021
L. Gao, J. Tow, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu, K. McDonell, N. Muennighoff, J. Phang, L. Reynolds, E. Tang, A. Thite, B. Wang, K. Wang, and A. Zou · 2021
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Classifier-free diffusion guidance
J. Ho and T. Salimans · 2021
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Editgan: High-precision semantic image editing
H. Ling, K. Kreis, D. Li, S. W. Kim, A. Torralba, and S. Fidler · 2021
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Scaling language models: Methods, analysis & insights from training gopher, 2021
J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, E. Rutherford, T. Hennigan, J. Menick, A. Cassirer, R. Powell, G. v. d. Driessche, L. A. Hendricks, M. Rauh, P.-S. Huang, A. Glaese, J. Welbl, S. Dathathri, S. Huang, J. Uesato, J. Mellor, I. Higgins, A. Creswell, N. McAleese, A. Wu, E. Elsen, S. Jayakumar, E. Buchatskaya, D. Budden, E. Sutherland, K. Simonyan, M. Paganini, L. Sifre, L. Martens, X. L. Li, A. Kuncoro, A. Nematzadeh, E. Gribovskaya, D. Donato, A. Lazaridou, A. Mensch, J.-B. Lespiau, M. Tsimpoukelli, N. Grigorev, D. Fritz, T. Sottiaux, M. Pajarskas, T. Pohlen, Z. Gong, D. Toyama, C. d. M. d’Autume, Y. Li, T. Terzi, V. Mikulik, I. Babuschkin, A. Clark, D. d. L. Casas, A. Guy, C. Jones, J. Bradbury, M. Johnson, B. Hechtman, L. Weidinger, I. Gabriel, W. Isaac, E. Lockhart, S. Osindero, L. Rimell, C. Dyer, O. Vinyals, K. Ayoub, J. Stanway, L. Bennett, D. Hassabis, K. Kavukcuoglu, and G. Irving · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
L. Reynolds and K. McDonell · 2021
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L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al · 2022
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Bloom: A 176b-parameter open-access multilingual language model
T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, M. Gallé, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, b. ichter, F. Xia, E. Chi, Q. V. Le, and D. Zhou · 2022
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Falcon-40B: an open large language model with state-of-the-art performance
E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, E. Goffinet, D. Heslow, J. Launay, Q. Malartic, B. Noune, B. Pannier, and G. Penedo · 2023
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Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo
Y. Anand, Z. Nussbaum, B. Duderstadt, B. Schmidt, and A. Mulyar · 2023
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The sciqa scientific question answering benchmark for scholarly knowledge
S. Auer, D. A. Barone, C. Bartz, E. G. Cortes, M. Y. Jaradeh, O. Karras, M. Koubarakis, D. Mouromtsev, D. Pliukhin, D. Radyush, et al · 2023
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Leace: Perfect linear concept erasure in closed form
N. Belrose, D. Schneider-Joseph, S. Ravfogel, R. Cotterell, E. Raff, and S. Biderman · 2023
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Pythia: A suite for analyzing large language models across training and scaling, 2023
S. Biderman, H. Schoelkopf, Q. Anthony, H. Bradley, K. O’Brien, E. Hallahan, M. A. Khan, S. Purohit, U. S. Prashanth, E. Raff, A. Skowron, L. Sutawika, and O. van der Wal · 2023
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Qlora: Efficient finetuning of quantized llms, 2023
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer · 2023
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K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz · 2023
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Openassistant conversations–democratizing large language model alignment
A. Köpf, Y. Kilcher, D. von Rütte, S. Anagnostidis, Z.-R. Tam, K. Stevens, A. Barhoum, N. M. Duc, O. Stanley, R. Nagyfi, et al · 2023
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Common diffusion noise schedules and sample steps are flawed, 2023
S. Lin, B. Liu, J. Li, and X. Yang · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
P. Manakul, A. Liusie, and M. J. Gales · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong · 2023
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B. Peng, C. Li, P. He, M. Galley, and J. Gao · 2023
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Trusting your evidence: Hallucinate less with context-aware decoding
W. Shi, X. Han, M. Lewis, Y. Tsvetkov, L. Zettlemoyer, and S. W.-t. Yih · 2023
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Sequentially controlled text generation
A. Spangher, X. Hua, Y. Ming, and N. Peng · 2023
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Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
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Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
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Self-consistency improves chain of thought reasoning in language models
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Wizardlm: Empowering large language models to follow complex instructions, 2023
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