Editing Language Model-Based Knowledge Graph Embeddings
Authors: Siyuan Cheng, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbin Liu, Huajun Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hypernetwork to edit/add facts. Our comprehensive experimental results reveal that KGEditor excels in updating specific facts without impacting the overall performance, even when faced with limited training resources. |
| Researcher Affiliation | Collaboration | Siyuan Cheng1, 2, 4*, Ningyu Zhang1, 2 , Bozhong Tian1, 2*, Xi Chen4*, Qingbin Liu4, Huajun Chen1, 2, 3 1Zhejiang University 2Zhejiang University Ant Group Joint Laboratory of Knowledge Graph 3Donghai Laboratory 4Platform and Content Group, Tencent |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Code and datasets will be available at https://github.com/Anonymous For Papers/Delta KG. |
| Open Datasets | Yes | We construct four datasets for EDIT and ADD tasks, based on two benchmarks FB15k-237 (Toutanova et al. 2015), and WN18RR (Dettmers et al. 2018). Code and datasets will be available at https://github.com/Anonymous For Papers/Delta KG. |
| Dataset Splits | Yes | Table 1: The statistics of datasets for the EDIT and ADD tasks. L-Test is the test set for knowledge locality to evaluate the rest of the knowledge in KGE. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names like Python 3.8, PyTorch 1.9, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |