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Long-context large language models (LLMs) hold promise for tasks such as question-answering (QA) over long documents, but they tend to miss important information in the middle of context documents (arXiv:2307.03172v3).
Know what you don’t know: Unanswerable questions for SQuAD
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Natural questions: A benchmark for question answering research
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Lost in the middle: How language models use long contexts
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Landmark attention: Random-access infinite context length for transformers
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Parallel context windows for large language models
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On position bias in summarization with large language models
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Found in the middle: Permutation self-consistency improves listwise ranking in large language models
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Focused transformer: Contrastive training for context scaling
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Extending context window of large language models via positional interpolation
Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. 2023a
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Longlora: Efficient fine-tuning of long-context large language models
Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia. 2023b
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Retrieval meets long context large language models
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro. 2024a
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Re-reading improves reasoning in language models
Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, and Jian-guang Lou. 2024b
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Jason Weston and Sainbayar Sukhbaatar. 2023 · 2023
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