The goal of knowledge graph completion (KGC) is to predict missing facts
among entities. Previous methods for KGC re-ranking are mostly built on
non-generative language models to obtain the probability of each candidate.
Recently, generative large language models (LLMs) have shown outstanding
performance on several tasks such as information extraction and dialog systems.
Leveraging them for KGC re-ranking is beneficial for leveraging the extensive
pre-trained knowledge and powerful generative capabilities. However, it may
encounter new problems when accomplishing the task, namely mismatch,
misordering and omission. To this end, we introduce KC-GenRe, a
knowledge-constrained generative re-ranking method based on LLMs for KGC. To
overcome the mismatch issue, we formulate the KGC re-ranking task as a
candidate identifier sorting generation problem implemented by generative LLMs.
To tackle the misordering issue, we develop a knowledge-guided interactive
training method that enhances the identification and ranking of candidates. To
address the omission issue, we design a knowledge-augmented constrained
inference method that enables contextual prompting and controlled generation,
so as to obtain valid rankings. Experimental results show that KG-GenRe
achieves state-of-the-art performance on four datasets, with gains of up to
6.7% and 7.7% in the MRR and Hits@1 metric compared to previous methods, and
9.0% and 11.1% compared to that without re-ranking. Extensive analysis
demonstrates the effectiveness of components in KG-GenRe.

KC-GenRe 是一种基于 LLMs 的知识约束生成重排序方法，用于知识图谱完成任务，通过生成的 LLMs 解决了匹配问题、排序问题和遗漏问题，并取得了优于以往方法的性能。

KC-GenRe: 基于大型语言模型的知识约束生成式重排序方法用于知识图谱补全

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on  Large Language Models for Knowledge Graph Completion

Recent advancements in diffusion models have showcased their impressive
capacity to generate visually striking images. Nevertheless, ensuring a close
match between the generated image and the given prompt remains a persistent
challenge. In this work, we identify that a crucial factor leading to the
text-image mismatch issue is the inadequate cross-modality relation learning
between the prompt and the output image. To better align the prompt and image
content, we advance the cross-attention with an adaptive mask, which is
conditioned on the attention maps and the prompt embeddings, to dynamically
adjust the contribution of each text token to the image features. This
mechanism explicitly diminishes the ambiguity in semantic information embedding
from the text encoder, leading to a boost of text-to-image consistency in the
synthesized images. Our method, termed MaskDiffusion, is training-free and
hot-pluggable for popular pre-trained diffusion models. When applied to the
latent diffusion models, our MaskDiffusion can significantly improve the
text-to-image consistency with negligible computation overhead compared to the
original diffusion models.

优化了扩散模型中文本和图像的一致性，通过引入自适应掩码调整文本令牌对图像特征的贡献，提高了合成图像的质量。