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Found 6 papers, 6 papers with code
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs.
Editing Large Language Models: Problems, Methods, and Opportunities
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information.
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
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DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
