370 citations · 1.8k across the 232 of their papers we have counts for
35 papers · 1 filter
Large Knowledge Model: Perspectives and Challenges
Huajun Chen
Humankind's understanding of the world is fundamentally linked to our perception and cognition, with \emph{human languages} serving as one of the major carriers of \emph{world know…
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Yichi Zhang, Zhuo Chen, Yin Fang +4
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especial…
Learning Invariant Molecular Representation in Latent Discrete Space
Xiang Zhuang, Qiang Zhang, Keyan Ding +5
Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when dat…
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
Zeyuan Wang, Qiang Zhang, Keyan Ding +4
Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address th…
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Zhiwei Huang, Juan Li, Long Jin +6
As the development of academic conferences fosters global scholarly communication, researchers consistently need to obtain accurate and up-to-date information about academic confer…
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Xiang Chen, Duanzheng Song, Honghao Gui +7
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in…
Can We Edit Multimodal Large Language Models?
Siyuan Cheng, Bozhong Tian, Qingbin Liu +4
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a hi…
Making Large Language Models Perform Better in Knowledge Graph Completion
Yichi Zhang, Zhuo Chen, Lingbing Guo +3
Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficien…
Editing Personality for Large Language Models
Shengyu Mao, Xiaohan Wang, Mengru Wang +4
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-relat…
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
Zhoubo Li, Ningyu Zhang, Yunzhi Yao +3
As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowl…
OceanGPT: A Large Language Model for Ocean Science Tasks
Zhen Bi, Ningyu Zhang, Yida Xue +4
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recentl…
Agents: An Open-source Framework for Autonomous Language Agents
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +15
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with e…
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Long Jin, Zhen Yao, Mingyang Chen +2
Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with speci…
When Do Program-of-Thoughts Work for Reasoning?
Zhen Bi, Ningyu Zhang, Yinuo Jiang +3
In the realm of embodied artificial intelligence, the reasoning capabilities of Large Language Models (LLMs) play a pivotal role. Although there are effective methods like program-…
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Peng Wang, Ningyu Zhang, Bozhong Tian +11
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to ou…
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhuo Chen, Lingbing Guo, Yin Fang +6
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting assoc…
Graph Sampling-based Meta-Learning for Molecular Property Prediction
Xiang Zhuang, Qiang Zhang, Bin Wu +3
Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been…
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang +5
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However,…
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +3
Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study emb…
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Lingbing Guo, Weiqing Wang, Zhuo Chen +5
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies. With the initial state of a system as input, the recent graph neural netwo…
Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian +5
Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a s…
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
Yuqi Zhu, Xiaohan Wang, Jing Chen +6
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experime…
Making Language Models Better Tool Learners with Execution Feedback
Shuofei Qiao, Honghao Gui, Chengfei Lv +3
Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their c…
Knowledge Rumination for Pre-trained Language Models
Yunzhi Yao, Peng Wang, Shengyu Mao +4
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted t…