Search Results for author: Chengjin Xu

Found 26 papers, 9 papers with code

Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models

1 code implementation18 Mar 2024 Yi Luo, Zhenghao Lin, Yuhao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong

Subsequently, the retrieval model correlates new inputs with relevant guidelines, which guide LLMs in response generation to ensure safe and high-quality outputs, thereby aligning with human values.

Response Generation Retrieval

Unlocking the Power of Large Language Models for Entity Alignment

no code implementations23 Feb 2024 Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, HuaWei Shen, Yuanzhuo Wang

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.

Code Translation Entity Alignment +2

ChartBench: A Benchmark for Complex Visual Reasoning in Charts

no code implementations26 Dec 2023 Zhengzhuo Xu, Sinan Du, Yiyan Qi, Chengjin Xu, Chun Yuan, Jian Guo

Multimodal Large Language Models (MLLMs) demonstrate impressive image understanding and generating capabilities.

Visual Reasoning

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

3 code implementations15 Jul 2023 Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, Jian Guo

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.

Hallucination Knowledge Graphs +3

Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?

no code implementations25 Jun 2023 Haohan Zhang, Fengrui Hua, Chengjin Xu, Jian Guo, Hao Kong, Ruiting Zuo

The rapid advancement of Large Language Models (LLMs) has led to extensive discourse regarding their potential to boost the return of quantitative stock trading strategies.

Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction

no code implementations10 Apr 2023 Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou

In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs).

Relation Temporal Knowledge Graph Completion

Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets

1 code implementation7 Apr 2023 Xuhui Jiang, Chengjin Xu, Yinghan Shen, Yuanzhuo Wang, Fenglong Su, Fei Sun, Zixuan Li, Zhichao Shi, Jian Guo, HuaWei Shen

Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios.

Entity Alignment Knowledge Graphs +1

Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph

no code implementations11 Feb 2023 Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, You Dou

Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting.

Knowledge Graph Embedding Knowledge Graphs +2

Ultrahyperbolic Knowledge Graph Embeddings

no code implementations1 Jun 2022 Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.

Knowledge Graph Embeddings

Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion

no code implementations18 Feb 2022 Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann

In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations.

Knowledge Graph Embeddings Link Prediction +2

Time-aware Relational Graph Attention Network for Temporal Knowledge Graph Embeddings

no code implementations29 Sep 2021 Chengjin Xu, Fenglong Su, Jens Lehmann

Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data.

Entity Alignment Graph Attention +2

Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings

no code implementations11 Apr 2021 Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann

For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.

Ensemble Learning Knowledge Graph Completion +3

Knowledge Graph Embeddings in Geometric Algebras

no code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann

Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.

Knowledge Graph Embeddings Knowledge Graphs +1

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

2 code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.

Knowledge Graph Embedding Link Prediction +1

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

2 code implementations18 Nov 2019 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.

Knowledge Graph Completion Knowledge Graph Embedding +5

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