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Language models are increasingly capable, yet still fail at a seemingly simple task of multi-digit multiplication.
Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2020
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Show your work: Scratchpads for intermediate computation with language models
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Nayoung Lee, Kartik Sreenivasan, Jason D Lee, Kangwook Lee, and Dimitris Papailiopoulos · 2023
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Gpt can solve mathematical problems without a calculator
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Language models do hard arithmetic tasks easily and hardly do easy arithmetic tasks
Andrew Gambardella, Yusuke Iwasawa, and Yutaka Matsuo · 2024
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Arithmetic without algorithms: Language models solve math with a bag of heuristics
Yaniv Nikankin, Anja Reusch, Aaron Mueller, and Yonatan Belinkov · 2024
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Physics of language models: Part 2.1, grade-school math and the hidden reasoning process
Tian Ye, Zicheng Xu, Yuanzhi Li, and Zeyuan Allen-Zhu · 2024
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Extrapolation by association: Length generalization transfer in transformers
Ziyang Cai, Nayoung Lee, Avi Schwarzschild, Samet Oymak, and Dimitris Papailiopoulos · 2025
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Yuntian Deng, Yejin Choi, and Stuart Shieber · 2024
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Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt
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Emergent linear representations in world models of self-supervised sequence models
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Iclr: In-context learning of representations
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana, Yongyi Yang, Maya Okawa, Kento Nishi, Martin Wattenberg, and Hidenori Tanaka
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Competition dynamics shape algorithmic phases of in-context learning
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