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In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, which makes it difficult to fit a sufficient number of examples in the prompt.
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Efficient Few-shot Learning Without Prompts
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An Explanation of In-context Learning as Implicit Bayesian Inference
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OPT: Open Pre-trained Transformer Language Models
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State-of-the-art generalisation research in NLP: a taxonomy and review
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What Makes Good In-context Examples for GPT-3?
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Rethinking the Role of Demonstrations: What Makes In-context Learning Work?
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Impact of Pretraining Term Frequencies on Few-shot Reasoning
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Learning To Retrieve Prompts for In-context Learning
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Few-shot Parameter-efficient Fine-tuning is Better and Cheaper than In-context Learning
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The Impact of Positional Encoding on Length Generalization in Transformers
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In-context Retrieval-augmented Language Models
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Larger language models do in-context learning differently
Jerry W. Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, and Tengyu Ma. 2023 · 2023
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