Graph Attention Networks (GATs) are designed to provide flexible neighborhood
aggregation that assigns weights to neighbors according to their importance. In
practice, however, GATs are often unable to switch off task-irrelevant
neighborhood aggregation, as we show experimentally and analytically. To
address this challenge, we propose GATE, a GAT extension that holds three major
advantages: i) It alleviates over-smoothing by addressing its root cause of
unnecessary neighborhood aggregation. ii) Similarly to perceptrons, it benefits
from higher depth as it can still utilize additional layers for (non-)linear
feature transformations in case of (nearly) switched-off neighborhood
aggregation. iii) By down-weighting connections to unrelated neighbors, it
often outperforms GATs on real-world heterophilic datasets. To further validate
our claims, we construct a synthetic test bed to analyze a model's ability to
utilize the appropriate amount of neighborhood aggregation, which could be of
independent interest.

GATE 是一种 GAT 的扩展，通过解决不必要的邻居聚合的根本原因，减轻了过平滑问题，并通过减小与不相关邻居的连接权重，在真实世界的异质数据集上常常优于 GAT。

GATE: 如何阻止侵入的邻居

GATE: How to Keep Out Intrusive Neighbors

Accurate knowledge selection is critical in knowledge-grounded dialogue
systems. Towards a closer look at it, we offer a novel perspective to organize
existing literature, i.e., knowledge selection coupled with, after, and before
generation. We focus on the third under-explored category of study, which can
not only select knowledge accurately in advance, but has the advantage to
reduce the learning, adjustment, and interpretation burden of subsequent
response generation models, especially LLMs. We propose GATE, a
generator-agnostic knowledge selection method, to prepare knowledge for
subsequent response generation models by selecting context-related knowledge
among different knowledge structures and variable knowledge requirements.
Experimental results demonstrate the superiority of GATE, and indicate that
knowledge selection before generation is a lightweight yet effective way to
facilitate LLMs (e.g., ChatGPT) to generate more informative responses.

GATE 是一种生成器无关的知识选择方法，它通过在不同的知识结构和可变的知识需求中选择与上下文相关的知识来为后续的响应生成模型准备知识，实验证明 GATE 的优越性，表明生成前的知识选择是一种轻量且有效的方式，可以促进 LLMs 生成更具信息量的响应。