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Large Language Models (LLMs) have demonstrated impressive real-world utility, exemplifying artificial useful intelligence (AUI).
On Information and Sufficiency
Kullback, S. and Leibler, R. A · 1951
Earlier work this paper cites.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
Miller, G. A · 1956
Earlier work this paper cites.
Constraints on learning and their role in language acquisition: Studies of the acquisition of American Sign Language
Newport, E. L · 1988
Earlier work this paper cites.
Learning and development in neural networks: The importance of starting small
Elman, J. L · 1993
Earlier work this paper cites.
The magical number 4 in short-term memory: a reconsideration of mental storage capacity
Cowan, N · 2001
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks, 2021
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., tau Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D · 2005
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition. corr
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2013
Earlier work this paper cites.
Analyzing the performance of multilayer neural networks for object recognition
Agrawal, P., Girshick, R., and Malik, J · 2014
Earlier work this paper cites.
Graves, A · 2014
Earlier work this paper cites.
Learning to see by moving
Agrawal, P., Carreira, J., and Malik, J · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
Earlier work this paper cites.
Dense optical flow prediction from a static image, 2015
Walker, J., Gupta, A., and Hebert, M · 2015
Earlier work this paper cites.
Neural turing machines: Convergence of copy tasks
Aleš, J · 2016
Earlier work this paper cites.
Human pose estimation with iterative error feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., and Malik, J · 2016
Earlier work this paper cites.
Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., Badia, A. P., Hermann, K. M., Zwols, Y., Ostrovski, G., Cain, A., King, H., Summerfield, C., Blunsom, P., Kavukcuoglu, K., and Hassabis, D · 2016
Earlier work this paper cites.
Learning image representations tied to ego-motion, 2016
Jayaraman, D. and Grauman, K · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Earlier work this paper cites.
A deep reinforced model for abstractive summarization
Paulus, R · 2017
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm, 2017
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., and Hassabis, D · 2017
Earlier work this paper cites.
Buy 4 reinforce samples, get a baseline for free!
Kool, W., van Hoof, H., and Welling, M · 2019
Earlier work this paper cites.
The cognitive benefits of learning computer programming: A meta-analysis of transfer effects
Scherer, R., Siddiq, F., and Sánchez Viveros, B · 2019
Earlier work this paper cites.
How does chunking help working memory?
Thalmann, M., Souza, A. S., and Oberauer, K · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M · 2020
Earlier work this paper cites.
Paaßen, B. and Schulz, A · 2020
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models, 2021
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Earlier work this paper cites.
Does pretraining for summarization require knowledge transfer?, 2021
Krishna, K., Bigham, J., and Lipton, Z. C · 2021
Earlier work this paper cites.
Pretrained transformers as universal computation engines, 2021
Lu, K., Grover, A., Abbeel, P., and Mordatch, I · 2021
Earlier work this paper cites.
Lime: Learning inductive bias for primitives of mathematical reasoning
Wu, Y., Rabe, M. N., Li, W., Ba, J., Grosse, R. B., and Szegedy, C · 2021
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Learning to see by looking at noise, 2022
Baradad, M., Wulff, J., Wang, T., Isola, P., and Torralba, A · 2022
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Data distributional properties drive emergent in-context learning in transformers
Chan, S., Santoro, A., Lampinen, A., Wang, J., Singh, A., Richemond, P., McClelland, J., and Hill, F · 2022
Cited alongside, same era.
A survey on in-context learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., et al · 2022
Cited alongside, same era.
Large language models are reasoning teachers
Ho, N., Schmid, L., and Yun, S.-Y · 2022
A survey on self-supervised learning: Algorithms, applications, and future trends
Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., and Tao, D · 2024
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Value augmented sampling for language model alignment and personalization, 2024
Han, S., Shenfeld, I., Srivastava, A., Kim, Y., and Agrawal, P · 2024
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V-star: Training verifiers for self-taught reasoners, 2024
Hosseini, A., Yuan, X., Malkin, N., Courville, A., Sordoni, A., and Agarwal, R · 2024
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Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al · 2024
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Disentangling memory and reasoning ability in large language models
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Cited alongside, same era.
Can language models learn from explanations in context?
Lampinen, A. K., Dasgupta, I., Chan, S. C., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., and Hill, F · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
Cited alongside, same era.
Defining and characterizing reward gaming
Skalse, J., Howe, N., Krasheninnikov, D., and Krueger, D · 2022
Cited alongside, same era.
Solving math word problems with process- and outcome-based feedback, 2022
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
Cited alongside, same era.
Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al · 2022
Cited alongside, same era.
Insights into pre-training via simpler synthetic tasks
Wu, Y., Li, F., and Liang, P. S · 2022
Cited alongside, same era.
Star: Bootstrapping reasoning with reasoning, 2022
Zelikman, E., Wu, Y., Mu, J., and Goodman, N. D · 2022
Cited alongside, same era.
Jin, M., Luo, W., Cheng, S., Wang, X., Hua, W., Tang, R., Wang, W. Y., and Zhang, Y · 2024
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Code pretraining improves entity tracking abilities of language models
Kim, N., Schuster, S., and Toshniwal, S · 2024
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Lost in the middle: How language models use long contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P · 2024
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Reft: Reasoning with reinforced fine-tuning
Luong, T. Q., Zhang, X., Jie, Z., Sun, P., Jin, X., and Li, H · 2024
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The llama 3 herd of models, 2024
Meta · 2024
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Microsoft · 2024
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Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models, 2024
Mirzadeh, I., Alizadeh, K., Shahrokhi, H., Tuzel, O., Bengio, S., and Farajtabar, M · 2024
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Go game archives - university of alberta, 2024
Müller, M · 2024
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OpenAI · 2024
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How does code pretraining affect language model task performance?
Petty, J., van Steenkiste, S., and Linzen, T · 2024
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Beyond human data: Scaling self-training for problem-solving with language models, 2024
Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., Parisi, A., Kumar, A., Alemi, A., Rizkowsky, A., Nova, A., Adlam, B., Bohnet, B., Elsayed, G., Sedghi, H., Mordatch, I., Simpson, I., Gur, I., Snoek, J., Pennington, J., Hron, J., Kenealy, K., Swersky, K., Mahajan, K., Culp, L., Xiao, L., Bileschi, M. L., Constant, N., Novak, R., Liu, R., Warkentin, T., Qian, Y., Bansal, Y., Dyer, E., Neyshabur, B., Sohl-Dickstein, J., and Fiedel, N · 2024
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Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., and Kim, Y · 2024
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On memorization of large language models in logical reasoning
Xie, C., Huang, Y., Zhang, C., Yu, D., Chen, X., Lin, B. Y., Li, B., Ghazi, B., and Kumar, R · 2024
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Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F., Dong, G., Wei, H., Lin, H., Tang, J., Wang, J., Yang, J., Tu, J., Zhang, J., Ma, J., Yang, J., Xu, J., Zhou, J., Bai, J., He, J., Lin, J., Dang, K., Lu, K., Chen, K., Yang, K., Li, M., Xue, M., Ni, N., Zhang, P., Wang, P., Peng, R., Men, R., Gao, R., Lin, R., Wang, S., Bai, S., Tan, S., Zhu, T., Li, T., Liu, T., Ge, W., Deng, X., Zhou, X., Ren, X., Zhang, X., Wei, X., Ren, X., Liu, X., Fan, Y., Yao, Y., Zhang, Y., Wan, Y., Chu, Y., Liu, Y., Cui, Z., Zhang, Z., Guo, Z., and Fan, Z · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Quiet-star: Language models can teach themselves to think before speaking, 2024
Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., and Goodman, N. D · 2024
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Raft: Adapting language model to domain specific rag, 2024
Zhang, T., Patil, S. G., Jain, N., Shen, S., Zaharia, M., Stoica, I., and Gonzalez, J. E · 2024
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Algorithmic capabilities of random transformers
Zhong, Z. and Andreas, J · 2024
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Languages: Befunge-93, 1993
Cat’s Eye Technologies · 2025
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Sft memorizes, rl generalizes: A comparative study of foundation model post-training, 2025
Chu, T., Zhai, Y., Yang, J., Tong, S., Xie, S., Schuurmans, D., Le, Q. V., Levine, S., and Ma, Y · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
DeepSeek-AI · 2025
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Cosmopedia: A guide to large language models
HuggingFace · 2025
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Brainfuck
Wikipedia · 2025
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