Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. |
| Approach: | They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance . |
| Outcome: | The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method . |
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| Challenge: | Existing benchmarks primarily evaluate long-context language models' retrieval capabilities. |
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
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| Challenge: | In-context learning (ICL) is a powerful new learning paradigm for Large Language Models (LLMs). |
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| Challenge: | Existing approaches address key factors that influence multilingual ICL, but they do not integrate them into the model. |
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| Challenge: | Recent advances in handling long sequences have unlocked new possibilities for long-context in-contact learning (ICL). |
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| Challenge: | In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations. |
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