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Predicting changes from scaling advanced AI systems is a desirable property for engineers, economists, governments and industry alike, and, while a well-established literature exists on how pretraining performance scales, predictable scaling behavior on downstream capabilities remains elusive.
A new measure of rank correlation
Kendall, M. G · 1938
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Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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The proof and measurement of association between two things
Spearman, C · 1961
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Survival Analysis: A Self-Learning Text
Kleinbaum, D. G. and Klein, M · 2012
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Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y · 2017
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triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E · 2017
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Crowdsourcing multiple choice science questions
Welbl, J., Liu, N. F., and Gardner, M · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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An empirical model of large-batch training, 2018
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Amini, A., Gabriel, S., Lin, S., Koncel-Kedziorski, R., Choi, Y., and Hajishirzi, H · 2019
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke, S., Ronan, L., Chandra, B., and Yejin, C · 2019
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Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Kelcey, M., Devlin, J., Lee, K., Toutanova, K. N., Jones, L., Chang, M.-W., Dai, A., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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A constructive prediction of the generalization error across scales
Rosenfeld, J. S., Rosenfeld, A., Belinkov, Y., and Shavit, N · 2019
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Social iqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., Le Bras, R., and Choi, Y · 2019
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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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“going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding
Zhou, B., Khashabi, D., Ning, Q., and Roth, D · 2019
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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The pile: An 800gb dataset of diverse text for language modeling, 2020
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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Scaling laws for autoregressive generative modeling
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Data and parameter scaling laws for neural machine translation
Gordon, M. A., Duh, K., and Kaplan, J · 2021
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Hernandez, D., Kaplan, J., Henighan, T., and McCandlish, S · 2021
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Scaling scaling laws with board games
Jones, A. L · 2021
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Revisiting neural scaling laws in language and vision, 2022
Alabdulmohsin, I., Neyshabur, B., and Zhai, X · 2022
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Unified scaling laws for routed language models
Clark, A., De Las Casas, D., Guy, A., Mensch, A., Paganini, M., Hoffmann, J., Damoc, B., Hechtman, B., Cai, T., Borgeaud, S., et al · 2022
Cited alongside, same era.
Predictability and surprise in large generative models
Ganguli, D., Hernandez, D., Lovitt, L., Askell, A., Bai, Y., Chen, A., Conerly, T., Dassarma, N., Drain, D., Elhage, N., et al · 2022
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
Later among the works it cites.
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AI, T · 2024
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Anthropic’s responsible scaling policy
Anthropic · 2024
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Introducing the next generation of claude
Anthropic · 2024
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Chinchilla scaling: A replication attempt, 2024
Besiroglu, T., Erdil, E., Barnett, M., and You, J · 2024
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Lessons from the trenches on reproducible evaluation of language models
Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., DiPofi, A., Etxaniz, J., Fattori, B., Forde, J. Z., Foster, C., Jaiswal, M., Lee, W. Y., Li, H., Lovering, C., Muennighoff, N., Pavlick, E., Phang, J., Skowron, A., Tan, S., Tang, X., Wang, K. A., Winata, G. I., Yvon, F., and Zou, A · 2024
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Scaling laws for reward model overoptimization, 2022
Gao, L., Schulman, J., and Hilton, J · 2022
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Scaling laws and interpretability of learning from repeated data
Hernandez, D., Brown, T., Conerly, T., DasSarma, N., Drain, D., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Henighan, T., Hume, T., et al · 2022
Cited alongside, same era.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Cited alongside, same era.
A solvable model of neural scaling laws
Maloney, A., Roberts, D. A., and Sully, J · 2022
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The inverse scaling prize, 2022
McKenzie, I., Lyzhov, A., Parrish, A., Prabhu, A., Mueller, A., Kim, N., Bowman, S., and Perez, E · 2022
Cited alongside, same era.
Scaling laws for a multi-agent reinforcement learning model
Neumann, O. and Gros, C · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al · 2022
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Survival function, 2023
contributors, W · 2024
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The 2024 economic report of the president, 03 2024
Council of Economic Advisers · 2024
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Deepseek llm: Scaling open-source language models with longtermism, 2024
DeepSeek-AI, :, Bi, X., Chen, D., Chen, G., Chen, S., Dai, D., Deng, C., Ding, H., Dong, K., Du, Q., Fu, Z., Gao, H., Gao, K., Gao, W., Ge, R., Guan, K., Guo, D., Guo, J., Hao, G., Hao, Z., He, Y., Hu, W., Huang, P., Li, E., Li, G., Li, J., Li, Y., Li, Y. K., Liang, W., Lin, F., Liu, A. X., Liu, B., Liu, W., Liu, X., Liu, X., Liu, Y., Lu, H., Lu, S., Luo, F., Ma, S., Nie, X., Pei, T., Piao, Y., Qiu, J., Qu, H., Ren, T., Ren, Z., Ruan, C., Sha, Z., Shao, Z., Song, J., Su, X., Sun, J., Sun, Y., Tang, M., Wang, B., Wang, P., Wang, S., Wang, Y., Wang, Y., Wu, T., Wu, Y., Xie, X., Xie, Z., Xie, Z., Xiong, Y., Xu, H., Xu, R. X., Xu, Y., Yang, D., You, Y., Yu, S., Yu, X., Zhang, B., Zhang, H., Zhang, L., Zhang, L., Zhang, M., Zhang, M., Zhang, W., Zhang, Y., Zhao, C., Zhao, Y., Zhou, S., Zhou, S., Zhu, Q., and Zou, Y · 2024
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Introducing the frontier safety framework
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Understanding emergent abilities of language models from the loss perspective, 2024
Du, Z., Zeng, A., Dong, Y., and Tang, J · 2024
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Language models scale reliably with over-training and on downstream tasks, 2024
Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., Xin, R., Nezhurina, M., Vasiljevic, I., Jitsev, J., Dimakis, A. G., Ilharco, G., Song, S., Kollar, T., Carmon, Y., Dave, A., Heckel, R., Muennighoff, N., and Schmidt, L · 2024
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Olmo: Accelerating the science of language models, 2024
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K. R., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M. E., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Strubell, E., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Zettlemoyer, L., Dodge, J., Lo, K., Soldaini, L., Smith, N. A., and Hajishirzi, H · 2024
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Predicting emergent abilities with infinite resolution evaluation, 2024
Hu, S., Liu, X., Han, X., Zhang, X., He, C., Zhao, W., Lin, Y., Ding, N., Ou, Z., Zeng, G., Liu, Z., and Sun, M · 2024
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Compression represents intelligence linearly, 2024
Huang, Y., Zhang, J., Shan, Z., and He, J · 2024
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Beyond probabilities: Unveiling the misalignment in evaluating large language models
Lyu, C., Wu, M., and Aji, A. F · 2024
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Emergent abilities in reduced-scale generative language models, 2024
Muckatira, S., Deshpande, V., Lialin, V., and Rumshisky, A · 2024
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Scaling data-constrained language models
Muennighoff, N., Rush, A., Barak, B., Le Scao, T., Tazi, N., Piktus, A., Pyysalo, S., Wolf, T., and Raffel, C. A · 2024
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Openai’s approach to frontier risk
OpenAI · 2024
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Hello gpt-4o
OpenAI · 2024
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Gpt-4 technical report, 2024
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How predictable is language model benchmark performance?, 2024
Owen, D · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.-b., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., et al · 2024
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Observational scaling laws and the predictability of language model performance, 2024
Ruan, Y., Maddison, C. J., and Hashimoto, T · 2024
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When scaling meets llm finetuning: The effect of data, model and finetuning method, 2024
Zhang, B., Liu, Z., Cherry, C., and Firat, O · 2024
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