4 citations · 5 across the 5 of their papers we have counts for
5 papers
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition
Yi Liu, Xiangrong Zhu, Xiangyu Liu +2
In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, m…
Knowledge Graph-Guided Retrieval Augmented Generation
Xiangrong Zhu, Yuexiang Xie, Yi Liu +2
Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing…
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
Yi Liu, Xiangyu Liu, Xiangrong Zhu +1
Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., "positive" from sentiment and "sport" from topic). For ease…
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning
Xiangrong Zhu, Guangyao Li, Wei Hu
Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding…
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph
Wei Cheng, Xiangrong Zhu, Wei Hu
Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers o…