Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge, knowledge unlearning can be applied to eliminate specific information while preserving the integrity of the remaining knowledge in KGs. Existing unlearning methods can generally be categorized into exact unlearning and approximate unlearning. However, exact unlearning requires high training costs, while approximate unlearning faces two issues when applied to KGs due to the inherent connectivity of triples: (1) It fails to fully remove targeted information, as forgetting triples can still be inferred from remaining ones. (2) It focuses on local data for specific removal, which weakens the remaining knowledge in the forgetting boundary. To address these issues, we propose GraphDPO, a novel approximate unlearning framework based on direct preference optimization (DPO). Firstly, to effectively remove forgetting triples, we reframe unlearning as a preference optimization problem, where the model is trained by DPO to prefer reconstructed alternatives over the original forgetting triples. This formulation penalizes reliance on forgettable knowledge, mitigating incomplete forgetting caused by KG connectivity. Moreover, we introduce an out-boundary sampling strategy to construct preference pairs with minimal semantic overlap, weakening the connection between forgetting and retained knowledge. Secondly, to preserve boundary knowledge, we introduce a boundary recall mechanism that replays and distills relevant information both within and across time steps. We construct eight unlearning datasets across four popular KGs with varying unlearning rates. Experiments show that GraphDPO outperforms state-of-the-art baselines by up to 10.1% in MRR_Avg and 14.0% in MRR_F1. Further analysis confirms that GraphDPO more effectively removes target knowledge while preserving surrounding context.

The rapid proliferation of documents has made document intelligence increasingly critical across various industries. In recent years, Large Language Models (LLMs) have dramatically transformed the field of document intelligence, allowing for more advanced and accurate document processing solutions. Despite these advancements, most existing surveys have failed to focus on these breakthroughs, instead concentrating on traditional methods and earlier machine learning techniques. This survey seeks to fill that gap by offering an in-depth analysis of approximately 300 papers published between 2021 and mid-2025, thus providing a comprehensive overview of the impact of LLMs in document intelligence. The key topics explored include Retrieval-Augmented Generation (RAG), long-context processing, and fine-tuning LLMs for document comprehension. Furthermore, the survey highlights essential datasets, practical applications, current challenges, and future research directions, offering critical insights for both researchers and industry practitioners looking to advance the field.

Knowledge graph reasoning (KGR) seeks to infer new factual triples from existing knowledge graphs (KGs). Recent methods have unified transductive and inductive reasoning by learning entity-independent representations through local neighboring structures. Nevertheless, these methods often encounter inefficiencies and rely on elaborate local structures without directly modeling the correlations between queries and various structures within KGs. In this paper, we propose a novel framework MulGA, which is designed to learn multi-granularity and adaptive embeddings for KGR. MulGA first employs connectivity subgraphs to uniformly and hierarchically represent query-related structures within KGs, such as triples, relation paths, and subgraphs, establishing the hierarchical relationship between structures at different granularities. Subsequently, we design a graph neural network-based multi-granularity embedding propagation module that unifies the message-passing process with the connectivity subgraph construction. This module obtains the query-related structural representations by all entities at multiple granularities, eliminating the need to explicitly extract any graph elements, thus addressing inefficiency issues. Moreover, we develop a structure-aware adaptive merging mechanism that assigns weights to different granularities and integrates them into cohesive subgraph-granularity representations for reasoning. The systematic experiments have been conducted on 15 benchmarks and MulGA achieves a significant improvement in MRR by an average of 0.5%-1.1% on transductive tasks and 0.2%-7.3% on inductive tasks than existing state-of-the-art methods. Moreover, MulGA exhibits faster convergence speed, smaller number of parameters, competitive inference time, and alleviates the over-smoothing prevalent in graph neural networks.

Previous entity linking methods in knowledge graphs (KGs) mostly link the textual mentions to corresponding entities. However, they have deficiencies in processing numerous multimodal data, when the text is too short to provide enough context. Consequently, we conceive the idea of introducing valuable information of other modalities, and propose a novel multimodal entity linking method with gated hierarchical multimodal fusion and contrastive training (GHMFC). Firstly, in order to discover the fine-grained inter-modal correlations, GHMFC extracts the hierarchical features of text and visual co-attention through the multi-modal co-attention mechanism: textual-guided visual attention and visual-guided textual attention. The former attention obtains weighted visual features under the guidance of textual information. In contrast, the latter attention produces weighted textual features under the guidance of visual information. Afterwards, gated fusion is used to evaluate the importance of hierarchical features of different modalities and integrate them into the final multimodal representations of mentions. Subsequently, contrastive training with two types of contrastive losses is designed to learn more generic multimodal features and reduce noise. Finally, the linking entities are selected by calculating the cosine similarity between representations of mentions and entities in KGs. To evaluate the proposed method, this paper releases two new open multimodal entity linking datasets: WikiMEL and Richpedia-MEL. Experimental results demonstrate that GHMFC can learn meaningful multimodal representation and significantly outperforms most of the baseline methods.

Sentiment and style control are two vital aspects in automatic poetry generation. Excellent Chinese classical poetry should express a certain emotion and embody a specific style at the same time. Existing work still has deficiencies in controlling sentiment and style simultaneously. To address above issues, in this paper, we propose a novel approach for Chinese classical poetry generation, which can generate sentiment-controllable and style-controllable poems. First, it classifies hundreds of thousands of poems by style, sentiment, format, and primary keyword. Then, it utilizes masking self-attention mechanism to associate multiple tags and verses. Besides, it can generate metrical rhyming verses with distinctive sentiment and style characteristics according to the tag-set and secondary keywords. Finally, this approach is applied in Chang Qing Yin, which can collaborate with users to polish generated poems, providing alternatives automatically. Experimental results show that our approach performs well in sentiment and style control, and quality of generated poems outperforms several strong baselines.