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Large language models have exhibited robust performance across diverse natural language processing tasks.
Catastrophic interference in connectionist networks: The sequential learning problem
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Efficient estimation of word representations in vector space
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Xlnet: Generalized autoregressive pretraining for language understanding
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Parameter-efficient transfer learning for nlp
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Language models are few-shot learners
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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The power of scale for parameter-efficient prompt tuning
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Llama: Open and efficient foundation language models
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Palm: Scaling language modeling with pathways
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Pangu- α \alpha : Large-scale autoregressive pretrained chinese language models with auto-parallel computation, 2021
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Glm-130b: An open bilingual pre-trained model, 2023
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A comprehensive study of knowledge editing for large language models
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