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Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.
Socialiqa: Commonsense reasoning about social interactions
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2006
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The winograd schema challenge
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A discourse-aware attention model for abstractive summarization of long documents
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2019 · 2019
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How long can open-source llms truly promise on context length?
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Landmark attention: Random-access infinite context length for transformers
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Mt-bench-101: A fine-grained benchmark for evaluating large language models in multi-turn dialogues
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