Fetching the paper…
Reading the bibliography…
Forecasts of future events are essential inputs into informed decision-making.
Principles of Forecasting: a Handbook for Researchers and Practitioners
Jon Scott Armstrong · 2001
Earlier work this paper cites.
Combining multiple probability predictions using a simple logit model
Ville A. Satopää, Jonathan Baron, Dean P. Foster, Barbara A. Mellers, Philip E. Tetlock, and Lyle H. Ungar · 2014
Earlier work this paper cites.
Identifying and cultivating superforecasters as a method of improving probabilistic predictions
Barbara Mellers, Eric Stone, Terry Murray, Angela Minster, Nick Rohrbaugh, Michael Bishop, Eva Chen, Joshua Baker, Yuan Hou, Michael Horowitz, Lyle Ungar, and Philip Tetlock · 2015
Earlier work this paper cites.
Superforecasting: The Art and Science of Prediction
Philip E. Tetlock and Dan Gardner · 2015
Earlier work this paper cites.
Uncertainty in forecasts of long-run economic growth
Peter Christensen, Kenneth Gillingham, and William Nordhaus · 2018
Earlier work this paper cites.
Special report: The simulations driving the world’s response to COVID-19
David Adam · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
Earlier work this paper cites.
Unsolved problems in ML safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2021
Earlier work this paper cites.
ForecastQA: A question answering challenge for event forecasting with temporal text data
Woojeong Jin, Rahul Khanna, Suji Kim, Dong-Ho Lee, Fred Morstatter, Aram Galstyan, and Xiang Ren · 2021
Earlier work this paper cites.
Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena · 2021
Earlier work this paper cites.
Data contamination: From memorization to exploitation
Inbal Magar and Roy Schwartz · 2022
Earlier work this paper cites.
Forecasting future world events with neural networks
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks · 2022
Earlier work this paper cites.
Humans vs large language models: Judgmental forecasting in an era of advanced AI
Mahdi Abolghasemi, Odkhishig Ganbold, and Kristian Rotaru · 2023
Earlier work this paper cites.
Model card and evaluations for Claude models, 2023
Anthropic · 2023
Earlier work this paper cites.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu · 2023
Earlier work this paper cites.
ForecastPFN: Synthetically-trained zero-shot forecasting
Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha Venkat Naidu, and Colin White · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team · 2023
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Anton Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
Cited alongside, same era.
Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks
Alon Jacovi, Avi Caciularu, Omer Goldman, and Yoav Goldberg · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
The claude 3 model family: Opus, sonnet, haiku, 2024
Anthropic · 2024
Closest in time.
Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source LLMs
Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, and Ondřej Dušek · 2024
Closest in time.
Chatbot arena: An open platform for evaluating llms by human preference, 2024
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica · 2024
Closest in time.
A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2024
Closest in time.
What’s in my big data?
Yanai Elazar, Akshita Bhagia, Ian Helgi Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Evan Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, et al · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
An open source data contamination report for large language models
Yucheng Li, Frank Guerin, and Chenghua Lin · 2023
Cited alongside, same era.
Artificial intelligence index report 2023
Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark, and Raymond Perrault · 2023
Cited alongside, same era.
Wisdom of the crowd vs. the best of the best of the best, 2023
Metaculus · 2023
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
Political instability patterns are obscured by conflict dataset scope conditions, sources, and coding choices
Clionadh Raleigh, Roudabeh Kishi, and Andrew Linke · 2023
Cited alongside, same era.
Lag-Llama: Towards foundation models for time series forecasting
Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Biloš, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, and Irina Rish · 2023
Cited alongside, same era.
Epoch AI · 2024
Closest in time.
Evaluating superhuman models with consistency checks
Lukas Fluri, Daniel Paleka, and Florian Tramer · 2024
Closest in time.
Moment: A family of open time-series foundation models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski · 2024
Closest in time.
Approaching human-level forecasting with language models
Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt · 2024
Closest in time.
Time-LLM: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen · 2024
Closest in time.
How predictable is language model benchmark performance?
David Owen · 2024
Closest in time.
Superhuman automated forecasting
Long Phan, Andrew Zeng, Mantas Mazeika, Adam Khoja, and Dan Hendrycks · 2024
Closest in time.
Can language models use forecasting strategies?
Sarah Pratt, Seth Blumberg, Pietro Kreitlon Carolino, and Meredith Ringel Morris · 2024
Closest in time.
Unified training of universal time series forecasting transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo · 2024
Closest in time.
Autocast++: Enhancing world event prediction with zero-shot ranking-based context retrieval
Qi Yan, Raihan Seraj, Jiawei He, Lili Meng, and Tristan Sylvain · 2024
Closest in time.
A careful examination of large language model performance on grade school arithmetic
Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, Will Song, Tiffany Zhao, Pranav Raja, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, and Summer Yue · 2024
Closest in time.