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We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation.
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The Netflix Prize
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Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
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Symbolic discovery of optimization algorithms
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Language Models are Few-Shot Learners
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Learning to Recommend Items to Wikidata Editors
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Billion-Scale Similarity Search with GPUs
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FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
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Efficient Memory Management for Large Language Model Serving with PagedAttention
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Recommender Systems with Generative Retrieval
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OpenP5: Benchmarking Foundation Models for Recommendation
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GenRec: Large Language Model for Generative Recommendation
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