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This paper proposes NEFTune, a simple trick by adding noise to embedding vectors during training which improve the outcome of instruction fine-tuning by large margin. If you are using SFT trainier by huggingface, you can use this trick by simply adding one line of code!
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This paper proposes knowledge prefix adapter (KoPA), an approach to improve structural-aware reasoning, thereby enhancing the reasoning ability of LLM.
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This paper introduces LLM-Planner, which utilizes LLMs as high-level planners for embodied agents, allowing them to generate and adapt plans according to the current environment. Experiments on the ALFRED dataset show that using less than 0.5% of paired training data, LLM-Planner achieves competitive performance with recent baselines that are trained using the full training data.
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This note includes insights from Yann LeCun, often referred to as the father of deep learning. In his talk, he discussed the limitations of current machine learning methods and self-supervised learning methods. He emphasized the need for objective-driven AI and introduced the concept of a modular cognitive architecture, also known as the world model. Additionally, he introduced the Joint-Embedding Predictive Architecture (JEPA), a new approach in the field.
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The paper proposes a new decoding method for open-ended text generation, called contrastive decoding (CD), which aims to generate text that is fluent, coherent, and informative, by exploiting the contrasts between expert model and amateur model behaviors.