3 citations · 7 across the 6 of their papers we have counts for
8 papers
Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
Huang Peng, Jiuyang Tang, Weixin Zeng +2
Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning…
CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C++ Compilation Repair
Weixuan Sun, Jucai Zhai, Dengfeng Liu +6
The automated repair of C++ compilation errors presents a significant challenge, the resolution of which is critical for developer productivity. Progress in this domain is constrai…
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
Hao Guo, Zihan Ma, Zhi Zeng +4
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake n…
Multi-modal Entity Alignment in Hyperbolic Space
Hao Guo, Jiuyang Tang, Weixin Zeng +2
Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi…
Towards Entity Alignment in the Open World: An Unsupervised Approach
Weixin Zeng, Xiang Zhao, Jiuyang Tang +3
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and qual…
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Weixin Zeng, Xiang Zhao, Jiuyang Tang +2
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be align…