Shi Wang

Due to the high energy density and long cycle life, lithium-ion batteries play an important role in electric transportation and energy storage systems. Therefore, accurate state of health (SOH) estimation is of great significance for battery lifetime management and safe operation. Existing SOH estimation methods are highly dependent on data and suffer from insufficient physical interpretability and limited cross-scenario generalization. To address the issues, introducing physical information constraints enables physical consistency requirements to be incorporated into the estimation process and confines the estimation to the physically feasible domain, thereby improving prediction performance and enhancing physical interpretability. From the perspective of sources of physical constraints and forms of constraint implementation, the review systematically summarizes the current research. Regarding the sources of constraints, equivalent circuit constraints, mechanism-based constraints, and degradation-dynamics constraints are introduced, and the advantages, disadvantages, and applicable scenarios of typical implementation forms, including physics-informed loss, physics-informed initialization, physics-driven architecture design, and virtual physics-driven fusion, are summarized. Finally, current challenges and future research directions are outlined based on a comprehensive comparison of existing studies, with the aim of providing a useful reference for future research on physics-informed SOH estimation.

In the relation extraction (RE) task, large language models (LLMs) have shown remarkable capabilities in predicting unknown relations, offering significant improvements in efficiency and flexibility over traditional methods. However, the probabilistic nature of the generation process in LLMs may lead to the occurrence of hallucinations, causing inaccurate relation triples be generated. To mitigate this problem, this paper proposes a novel weakly supervised method, Cross-Attention Contrastive Relation Extraction (CACRE), which aims at detecting erroneous relation triples generated by LLMs and then effectively distinguishing valid ones. The CACRE leverages contrastive learning and cross-attention mechanisms. Specifically, contrastive learning is applied to distinguish between positive and negative relation triples, enhancing the model’s feature extraction capability by learning discriminative features. Subsequently, a cross-attention mechanism is employed to capture the semantic associations between texts and triples, thereby improving the model’s ability to understand and extract information from the input content. Experiment results on the DuIE2.0 dataset and the TACRED dataset demonstrate that CACRE significantly outperforms baseline LLMs, with average improvements of 12% and 8% in precision, respectively.

Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning capabilities. LLM-enhanced KGC methods primarily focus on designing task-specific instructions, achieving promising advancements. However, there are still two critical challenges. First, existing methods often ignore the inconsistent representation spaces between natural language and graph structures. Second, most approaches design separate instructions for different KGC tasks, leading to duplicate works and time-consuming processes. To address these challenges, we propose SAT, a novel framework that enhances LLMs for KGC via structure-aware alignment-tuning. Specifically, we first introduce hierarchical knowledge alignment to align graph embeddings with the natural language space through multi-task contrastive learning. Then, we propose structural instruction tuning to guide LLMs in performing structure-aware reasoning over KGs, using a unified graph instruction combined with a lightweight knowledge adapter. Experimental results on two KGC tasks across four benchmark datasets demonstrate that SAT significantly outperforms state-of-the-art methods, especially in the link prediction task with improvements ranging from 8.7% to 29.8%.

Yu Liu, Yanan Cao, Xixun Lin, Yanmin Shang, Shi Wang, Shirui Pan. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The rapid development of large language models (LLMs) has significantly improved the quality and diversity of AI-generated content(AIGC). LLMs-generated text detection plays an important role in preventing the harmful misuse of large language models. Although many detection methods have been proposed, they all have room for improvement and lack generality when detecting multiple models. To tackle these drawbacks, an unsupervised-based Structural Information for LLM-generated Text Detection(SILTD) method is proposed. First, we construct a multi-relational text graph based on the similarity of text features, which aims to model the intricate similarities and correlations between texts in the generative statistical space. Second, we propose a novel unsupervised graph clustering method. The multi-relational graph is transformed into an encoding tree, which is then optimized based on a two-dimensional structure entropy minimization algorithm to achieve hierarchical clustering of texts. Structural entropy minimization enables achieving high-quality clusters, by measuring the uncertainty of random walks within the graph. Finally, we introduce a new method that measures text similarity and computes the intensity of text aggregation within each cluster, to perform in-cluster label inference. Extensive experiments show that, compared to baseline methods, our approach is more effective and generalizable in detecting six popular LLMs across three datasets.

Answering complex logical queries over knowledge graphs (KGs) is a fundamental yet challenging task. Recently, query representation has been a mainstream approach to complex logical reasoning, making the target answer and query closer in the embedding space. However, there are still two limitations. First, prior methods model the query as a fixed vector, but ignore the uncertainty of relations on KGs. In fact, different relations may contain different semantic distributions. Second, traditional representation frameworks fail to capture the joint distribution of queries and answers, which can be learned by generative models that have the potential to produce more coherent answers. To alleviate these limitations, we propose a novel generative model, named DiffCLR, which exploits the diffusion model for complex logical reasoning to approximate query distributions. Specifically, we first devise a query transformation to convert logical queries into input sequences by dynamically constructing contextual subgraphs. Then, we integrate them into the diffusion model to execute a multi-step generative process, and a structure-enhanced self-attention is further designed for incorporating the structural features embodied in KGs. Experimental results on two benchmark datasets show our model effectively outperforms state-of-the-art methods, particularly in multi-hop chain queries with significant improvement.