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Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applications.
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Albert: A lite bert for self-supervised learning of language representations
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
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Language models are few-shot learners
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Training language models to follow instructions with human feedback
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Mastering atari, go, chess and shogi by planning with a learned model
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GLM-130B: an open bilingual pre-trained model
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Cold-attack: Jailbreaking llms with stealthiness and controllability
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A survey on data selection for language models, 2024
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