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Forecasting future events is important for policy and decision making.
Verification of forecasts expressed in terms of probability
Brier, G. W. (1950) · 1950
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Principles of Forecasting: a Handbook for Researchers and Practitioners
Armstrong, J. S. (2001) · 2001
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
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Logarithmic markets coring rules for modular combinatorial information aggregation
Hanson, R. (2007) · 2007
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Superforecasting: The Art and Science of Prediction
Tetlock, P. E. and Gardner, D. (2015) · 2015
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Formal specification of constant product (xy= k) market maker model and implementation
Zhang, Y., Chen, X., and Park, D. (2018) · 2018
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Multi-hop reading comprehension through question decomposition and rescoring
Min, S., Zhong, V., Zettlemoyer, L., and Hajishirzi, H. (2019) · 2019
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Special report: The simulations driving the world’s response to COVID-19
Adam, D. (2020) · 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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020) · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Lewis, P. S. H., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D. (2020) · 2020
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A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, W., Bamler, R., and Zhu, X. X. (2021) · 2021
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Unsolved problems in ML safety
Hendrycks, D., Carlini, N., Schulman, J., and Steinhardt, J. (2021) · 2021
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Leveraging passage retrieval with generative models for open domain question answering
Izacard, G. and Grave, É. (2021) · 2021
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ForecastQA: A question answering challenge for event forecasting with temporal text data
Jin, W., Khanna, R., Kim, S., Lee, D.-H., Morstatter, F., Galstyan, A., and Ren, X. (2021) · 2021
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WebGPT: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., Jiang, X., Cobbe, K., Eloundou, T., Krueger, G., Button, K., Knight, M., Chess, B., and Schulman, J. (2021) · 2021
Cited alongside, same era.
Show your work: Scratchpads for intermediate computation with language models
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., and Odena, A. (2021) · 2021
Cited alongside, same era.
Retrieval augmentation reduces hallucination in conversation
Shuster, K., Poff, S., Chen, M., Kiela, D., and Weston, J. (2021) · 2021
Cited alongside, same era.
Maniswap
Manifold (2022) · 2022
Cited alongside, same era.
Forecasting future world events with neural networks
Zou, A., Xiao, T., Jia, R., Kwon, J., Mazeika, M., Li, R., Song, D., Steinhardt, J., Evans, O., and Hendrycks, D. (2022) · 2022
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J. (2023) · 2023
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OpenAI (2023) · 2023
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Polymarket/poly-market-maker: Market Maker Keeper for the polymarket CLOB
Polymarket (2023) · 2023
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Lag-Llama: Towards foundation models for time series forecasting
Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., Biloš, M., Ghonia, H., Hassen, N. V., Schneider, A., Garg, S., Drouin, A., Chapados, N., Nevmyvaka, Y., and Rish, I. (2023) · 2023
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Large language model prediction capabilities: Evidence from a real-world forecasting tournament
Schoenegger, P. and Park, P. S. (2023) · 2023
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Cited alongside, same era.
Humans vs large language models: Judgmental forecasting in an era of advanced AI
Abolghasemi, M., Ganbold, O., and Rotaru, K. (2023) · 2023
Cited alongside, same era.
Model card and evaluations for Claude models
Anthropic (2023) · 2023
Cited alongside, same era.
Universal self-consistency for large language model generation
Chen, X., Aksitov, R., Alon, U., Ren, J., Xiao, K., Yin, P., Prakash, S., Sutton, C., Wang, X., and Zhou, D. (2023) · 2023
Cited alongside, same era.
Rephrase and respond: Let large language models ask better questions for themselves
Deng, Y., Zhang, W., Chen, Z., and Gu, Q. (2023) · 2023
Cited alongside, same era.
ForecastPFN: Synthetically-trained zero-shot forecasting
Dooley, S., Khurana, G. S., Mohapatra, C., Naidu, S. V., and White, C. (2023) · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team (2023) · 2023
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M. A., Qiu, S., and Wilson, A. G. (2023) · 2023
Cited alongside, same era.
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T. (2023) · 2023
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Calibration in deep learning: A survey of the state-of-the-art
Wang, C. (2023) · 2023
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A decoder-only foundation model for time-series forecasting
Das, A., Kong, W., Sen, R., and Zhou, Y. (2024) · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E. (2024) · 2024
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Time-LLM: Time series forecasting by reprogramming large language models
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q. (2024) · 2024
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Unified training of universal time series forecasting transformers
Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., and Sahoo, D. (2024) · 2024
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Autocast++: Enhancing world event prediction with zero-shot ranking-based context retrieval
Yan, Q., Seraj, R., He, J., Meng, L., and Sylvain, T. (2024) · 2024
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Large language models for information retrieval: A survey
Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. (2024) · 2024
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