Large language models (LLMs) have encoded vast amounts of knowledge in their parameters, but the acquired knowledge can sometimes be incorrect or outdated over time, necessitating rectification after pre-training. Traditional localized methods in knowledge-based model editing (KME) typically assume that knowledge is stored in particular intermediate layers. However, recent research suggests that these methods do not identify the optimal locations for parameter editing, as knowledge gradually accumulates across all layers in LLMs during the forward pass rather than being stored in specific layers. This paper, for the first time, introduces the concept of critical transmission paths into KME for parameter updating. Specifically, these paths capture the key information flows that significantly influence the model predictions for the editing process. To facilitate this process, we also design a parameter-aware contrastive rectifying algorithm that considers less important paths as contrastive examples. Experiments on two prominent datasets and three widely used LLMs demonstrate the superiority of our method in editing performance.

Taxonomy expansion is a primary method for enriching taxonomies, involving appending a large number of additional nodes (i.e., queries) to an existing taxonomy (i.e., seed), with the crucial step being the identification of the appropriate anchor (parent node) for each query by incorporating the structural information of the seed. Despite advancements, existing research still faces an inherent challenge of spurious query-anchor matching, often due to various interference factors (e.g., the consistency of sibling nodes), resulting in biased identifications. To address the bias in taxonomy expansion caused by unobserved factors, we introduce the Structural Causal Model (SCM), known for its bias elimination capabilities, to prevent these factors from confounding the task through backdoor paths. Specifically, we employ the Front-Door Criterion, which guides the decomposition of the expansion process into a parser module and a connector. This enables the proposed causal-aware Taxonomy Expansion model to isolate confounding effects and reveal the true causal relationship between the query and the anchor. Extensive experiments on three benchmarks validate the effectiveness of TEF, with a notable 6.1% accuracy improvement over the state-of-the-art on the SemEval16-Environment dataset.

Large language models (LLMs) have shown significant achievements in solving a wide range of tasks. Recently, LLMs’ capability to store, retrieve and infer with symbolic knowledge has drawn a great deal of attention, showing their potential to understand structured information. However, it is not yet known whether LLMs can understand Description Logic (DL) ontologies. In this work, we empirically analyze the LLMs’ capability of understanding DL-Lite ontologies covering 6 representative tasks from syntactic and semantic aspects. With extensive experiments, we demonstrate both the effectiveness and limitations of LLMs in understanding DL-Lite ontologies. We find that LLMs can understand formal syntax and model-theoretic semantics of concepts and roles. However, LLMs struggle with understanding TBox NI transitivity and handling ontologies with large ABoxes. We hope that our experiments and analyses provide more insights into LLMs and inspire to build more faithful knowledge engineering solutions.