Search Results for author:
Found 10 papers, 5 papers with code
Using Item Response Theory to Measure Gender and Racial Bias of a BERT-based Automated English Speech Assessment System
This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment.
Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Recent advances in Multi-modal Large Language Models (MLLMs), such as LLaVA-series models, are driven by massive machine-generated instruction-following data tuning.
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning
Empathetic response generation endows agents with the capability to comprehend dialogue contexts and react to expressed emotions.
Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints
Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?)
DuetRAG: Collaborative Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks.
Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion
Temporal Knowledge Graph Completion
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph.
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation
Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion.
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
