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arXiv:2407.19231v1 Announce Type: new Abstract: Graph neural networks (GNNs) have achieved significant success in various applications. Most GNNs learn the node features with information aggregation of its neighbors and feature transformation in each layer. However, the node features become indistinguishable after many layers, leading to performance deterioration: a significant limitation known as over-smoothing. Past work adopted various techniques for addressing this issue, such as normalization and skip-connection of layer-wise output. After the study, we found that the information aggregations in existing work are all contracted aggregations, with the intrinsic property that features will inevitably converge to the same single point…

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[Submitted on 28 Jul 2024 (v1), last revised 30 Jul 2024 (this version, v2)] View a PDF of the paper titled Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge, by Tianhao Wu and 7 other authors View PDF Abstract:Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs can improve by judging their own responses instead of relying on human labelers. However, existing methods have primarily focused on improving model responses rather than judgment capabilities, resulting in rapid…