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This is a summary post of this paper: https://arxiv.org/abs/2305.06474 Why this paper/ Goal of this paper? As LLMs have the following properties: Large-scale knowledge and real-world information Strong generalization ability through effective few-shot learning Strong reasons capability with chain-of-thought, self-consistency, etc. Key question answered by this paper: Can we use LLMs for recommender systems? RQ1: Do off-the-shelf LLMs perform well for zero-shot and few-shot recommendations? RQ2: How do LLMs compare with the traditional recommenders in a fair setting?...

Gemini is a family of large language models (LLMs) developed by Google AI. They are designed to be more powerful and flexible than previous LLMs, capable of handling a wider range of tasks and data formats. Here’s a detailed breakdown of key aspects: Model(s) Architecture: Multimodal Encoders: Encoders for specific data types i.e. text, audio, image to convert data to a common representation that the model can understand. Early/Late Fusion: Combines the output from various encoders and allows the model to learn the relationship between different modalities....