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The significant progress of large language models (LLMs) provides a promising opportunity to build human-like systems for various practical applications.
Language models are few-shot learners
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Language models as recommender systems: Evaluations and limitations
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Palm: Scaling language modeling with pathways
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M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
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Large language models are zero-shot rankers for recommender systems
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Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
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Few-shot learning with retrieval augmented language models
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Training language models to follow instructions with human feedback
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Emergent abilities of large language models
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Chain-of-thought prompting elicits reasoning in large language models
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Llama: Open and efficient foundation language models
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Llama 2: Open foundation and fine-tuned chat models
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Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
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A Survey on Large Language Models for Recommendation
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Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
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Improving Language Models via Plug-and-Play Retrieval Feedback
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Recommendation as instruction following: A large language model empowered recommendation approach
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