01-Mi Querencia (Simón Díaz)\n02-Tonada De Luna Llena (Simón Díaz)\n03-Sabana (José Salazar/Simón Díaz)\n04-Caballo Viejo (Simón Díaz)\n05-Todo Este Campo Es Mío (Simón Díaz)\n06-La Pena Del Becerrero (Simón Díaz)\n07
Pursuant to 5TH CIR. R. 47.5, the court has determined\n that this opinion should not be published and is not precedent\n except under the limited circumstances set forth in 5TH CIR.\n
...from opening a through road or street for public use across said public park in the Park of The City of Riverton." (Emphasis supplied.)\nAppealing from that order, the city asserts (1) plaintiffs have no standing or right to maintain the action; (2) that the proposed road was in an undedicated part of the park; (3) that the proposed road was an access road and not a through street or part of the city's street system.(4
TO PERFORM QUADRATIC REGRESSION\nON THE TI84 GRAPHING CALCULATOR.\nDETERMINE HOW WELL THE REGRESSION MODEL FITS THE DATA.\nAND THEN MAKE PREDICTIONS USING THE REGRESSION EQUATION.\nIN STATISTICS, REGRESSION ANALYSIS INCLUDES\nANY TECHNIQUES USED FOR MODELING \n
4. Introduction\n5. Chapter 1: What Is Trust?\n6. Chapter 2: Trust Brings Rest\n7. Chapter 3: Who Can I Trust?\n8. Chapter 4: The Folly of Self-Reliance\n9. Chapter 5: Trust God and Do Good (Part 1)\n10. Chapter 6: Trust God and Do Good (Part 2)\n11. Chapter 7: At All Times\n12. Chapter 8
creddump is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\ncreddump is distributed in the hope that it will be useful.\n
The chosen sites were recorded as:0 = sound (n = 13);1 = first visible sign of noncavitated lesion seen only when the tooth is dried;2 = visible noncavitated lesion seen when wet and dry;3 = microcavitation in enamel;4 = noncavitated lesion extending into dentine seen as an undermining shadow;5 = small cavitated lesion with visible dentine (less than 50% of surface);6
/**\nCopyright (c) 2019. The Android Open Source Project\n\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\n
children have a lack of maturity and an underdeveloped\n sense of responsibility, leading to recklessness, impul-\nsivity, and heedless risk-taking... Second, children\n are more vulnerable to negative influences and\n outside pressures, including from their family and\n peers; they have limited control over their own envi-\n
1. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent Abilities of Large Language Models (arXiv:2206.07682). arXiv. https://doi.org/10.48550/arXiv.2206.076822. P. W. Anderson,More Is Different.Science, 177(4047): 393-396, 1972. DOI:10.1126/science.177.4047.3933. Jacob Steinhardt. Future ml systems will be qualitatively different, 2022. URL https://bounded-regret. ghost.io/future-ml-systems-will-be-qualitatively-different/. Accessed May 20, 2022.4. Bernardo A. Huberman and Tad Hogg. Phase transitions in artificial intelligence systems. Artificial Intelligence, 33(2):155–171, 1987. URL https://www.sciencedirect.com/science/article/ abs/pii/0004370287900336.5. Jared Kaplan, Sam McCandlish, Tom Henighan, et al. Scaling Laws for Neural Language Models. arXiv:2001.08361, 20206. Ziming Liu, Ouail Kitouni, Niklas Nolte, et al. Towards Understanding Grokking: An Effective Theory of Representation Learning. arXiv:2205.10343, 20227. Preetum Nakkiran, Gal Kaplun, Yamini Bansal, et al. Deep Double Descent: Where Bigger Models and More Data Hurt. arXiv:1912.02292, 20198. Huang, Y., Hu, S., Han, X., Liu, Z., & Sun, M. (2024). Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition (arXiv:2402.15175). arXiv. https://doi.org/10.48550/arXiv.2402.151759. Wei, Jason, et al. "Chain-of-thought prompting elicits reasoning in large language models." Advances in neural information processing systems 35 (2022): [[1]].10. Wei, Jason, et al. "Finetuned language models are zero-shot learners." arXiv preprint arXiv:2109.01652 (2021).11. Srivastava, Aarohi, et al. "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models." arXiv preprint arXiv:2206.04615 (2022).12. 张俊林, 大语言模型的涌现能力：现象与解释. 2023. 知乎. https://zhuanlan.zhihu.com/p/62143865313. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. URL https://arxiv.org/abs/2001.0836114. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. NeurIPS, 2022. URL https://arxiv.org/abs/2203.15556.15. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. URL https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html16. Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. Scaling language models: Methods, analysis & insights from training Gopher. arXiv preprint arXiv:2112.11446, 2021. URL https://arxiv.org/abs/ 2112.11446.17. BIG-Bench. Beyond the imitation game: Measuring and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022. URL https://arxiv.org/abs/2206.04615.18. Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H-T., Jin, A., Bos, T., Baker, L., Du, Y. et al. LaMDA: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022. URL https://arxiv.org/abs/2201.08239.19. Lin, S., Hilton, J., & Evans, O. TruthfulQA: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958, 2021. URL https://arxiv.org/abs/2109.07958.20. Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S. et al. Scaling language models: Methods, analysis and insights from training Gopher. arXiv preprint arXiv:2112.11446, 2021. URL https://arxiv.org/abs/ 2112.11446.21. Patel, R. & Pavlick, E. Mapping language models to grounded conceptual spaces. ICLR, 2022. URL https://openreview.net/forum?id=gJcEM8sxHK.22. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. Measuring massive multitask language understanding. ICLR, 2021a. URL https://openreview.net/ forum?id=d7KBjmI3GmQ.23. Pilehvar, M. T. and Camacho-Collados, J. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. NAACL, 2019. URL https://aclanthology.org/N19-1128.24. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E. et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021. URL https://arxiv.org/abs/2108.07258.25. Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D. et al. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.00114, 2021. URL https://openreview.net/forum?id=iedYJm92o0a.26. Cobbe, K., Kosaraju, V., Bavarian, M., Hilton, J., Nakano, R., Hesse, C., & Schulman, J. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. URL https://arxiv.org/abs/2110.14168.27. Suzgun, M., Scales, N., Scharli, N., Gehrmann, S., Tay, Y., Chung, H.Y., Chowdhery, A., Le, Q.V., Chi, E.H., Zhou, D., & Wei, J. Challenging BIG-Bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022. URL https://arxiv.org/abs/2210.09261.28. Lampinen, A.k., Dasgupta, I., Chan, S. C.Y., Matthewson, K., Tessler, M.H., Creswell, A., McClelland, J.L., Wang, J.X., & Hill, F. Can language models learn from explanations in context? Findings of EMNLP, 2022. URL https://arxiv.org/abs/2204.02329.29. Ouyang,L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A. et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022. URL https://arxiv.org/abs/2203.02155.30. Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T.L., Raja, A. et al. Multitask prompted training enables zero-shot task generalization. ICLR, 2022. URL https://openreview.net/forum?id=9Vrb9D0WI4.31. Chung, H.W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S. et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. URL https://arxiv.org/abs/2210.11416.32. Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, E., Perez, E., Schiefer, N., Hatfield-Dodds Z., et al. (2022) Language Models (Mostly) Know What They Know.(arXiv:2207.05221). arXiv. https://arxiv.org/abs/2207.0522133. Schaeffer, R., Miranda, B., & Koyejo, S. (2023). Are Emergent Abilities of Large Language Models a Mirage? (arXiv:2304.15004). arXiv. https://doi.org/10.48550/arXiv.2304.1500434. Michaud, E. J., Liu, Z., Girit, U., & Tegmark, M. (n.d.). The Quantization Model of Neural Scaling. https://arxiv.org/abs/2303.1350635. Lubana, E. S., Kawaguchi, K., Dick, R. P., & Tanaka, H. (2024). A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language (arXiv:2408.12578). arXiv. https://doi.org/10.48550/arXiv.2408.1257836. E. P. Hoel, L. Albantakis, and G. Tononi. Quantifying causal emergence shows that macro can beat micro. Proceedings of the National Academy of Sciences, 110(49):[[2]], 2013.37. E. P. Hoel, L. Albantakis, W. Marshall, and G. Tononi. Can the macro beat the micro? integrated information across spatiotemporal scales. Neuroscience of Consciousness, 2016(1):niw012, 2016.38. F. E. Rosas, P. A. Mediano, H. J. Jensen, A. K. Seth, A. B. Barrett, R. L. Carhart-Harris, and D. Bor. Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLoS computational biology, 16(12):e1008289, 2020.39. Chen, H., Yang, X., Zhu, J., & Wang, W. (2024). Quantifying Semantic Emergence in Language Models (arXiv:2405.12617). arXiv.40. M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm. Mutual information neural estimation. In International conference on machine learning, pages531–540. PMLR, 2018.41. Lindsey, et al., "On the Biology of a Large Language Model", Transformer Circuits, 2025.42. Ameisen, et al., "Circuit Tracing: Revealing Computational Graphs in Language Models", Transformer Circuits, 2025.
