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This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs).
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Compositional questions do not necessitate multi-hop reasoning
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Language models show human-like content effects on reasoning
Ishita Dasgupta, Andrew K Lampinen, Stephanie CY Chan, Antonia Creswell, Dharshan Kumaran, James L McClelland, and Felix Hill. 2022 · 2022
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022
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Large language models are zero-shot reasoners
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End-to-end segmentation-based news summarization
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Scrolls: Standardized comparison over long language sequences
Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, et al. 2022 · 2022
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Scaling laws vs model architectures: How does inductive bias influence scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar, Hyung Won Chung, William Fedus, Jinfeng Rao, Sharan Narang, Vinh Q Tran, Dani Yogatama, and Donald Metzler. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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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 · 2023
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Guiding llm to fool itself: Automatically manipulating machine reading comprehension shortcut triggers
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Loogle: Can long-context language models understand long contexts?
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OpenAI. 2023 · 2023
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Training trajectories of language models across scales
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Large language models are human-level prompt engineers
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Zeroscrolls: A zero-shot benchmark for long text understanding
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Large language models can be easily distracted by irrelevant context
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The impact of reasoning step length on large language models
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