Weak-to-strong generalization
We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
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Large language models (LLMs) have made impressive advancements in generating coherent text for various activities and domains, including grammatical error correction (GEC), text simplification, paraphrasing, and style transfer. One of the emerging skills of LLMs is their ability to generalize and perform tasks that they have never seen before. To achieve this, LLMs are fine-tuned…
There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. ViTs suffer from quadratic computational complexity while excelling in fitting capabilities and international receptive field. On the other hand, CNNs offer scalability and linear complexity…
Pretrained on trillion-token corpora, large neural language models (LLMs) have achieved remarkable performance strides (Touvron et al., 2023a; Geng & Liu, 2023). However, the scalability benefits of such data for traditional n-gram language models (LMs) still need to be explored. This paper from the University of Washington and Allen Institute for Artificial Intelligence delves into…
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Large Language Models are being used in various fields. With the growth of AI, the use of LLMs has further increased. They are used in various applications together with those that require reasoning, such as answering multiple-turn questions, completing tasks, and generating code. However, these models are not completely reliable as they may provide inaccurate…