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Found 19 papers, 8 papers with code
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases
Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints.
A Survey on LLM-as-a-Judge
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale.
Unlocking the Power of Large Language Models for Entity Alignment
To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.
Qsnail: A Questionnaire Dataset for Sequential Question Generation
Questionnaires entail a series of questions that must conform to intricate constraints involving the questions, options, and overall structure.
A Survey on Large Language Model Hallucination via a Creativity Perspective
Hallucinations in large language models (LLMs) are always seen as limitations.
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources.
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified knowledge.
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios.
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity).
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.
NeuInfer: Knowledge Inference on N-ary Facts
It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Information selection is the most important component in document summarization task.
Ranked #32 on
Abstractive Text Summarization
on CNN / Daily Mail
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #43 on
Abstractive Text Summarization
on CNN / Daily Mail
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
Document-level information is very important for event detection even at sentence level.
Path-Based Attention Neural Model for Fine-Grained Entity Typing
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.
Efficient Parallel Translating Embedding For Knowledge Graphs
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.
Ranked #1 on
Link Prediction
on WN18 (filtered)
Locally Adaptive Translation for Knowledge Graph Embedding
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.
