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20232026
most citedA Comprehensive Study of Knowledge Editing for Large Language Models

21 citations · 28 across the 22 of their papers we have counts for

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11 papers · 1 filter

cs.CL2024

OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System

Yujie Luo, Xiangyuan Ru, Kangwei Liu +10

We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news,…

cs.CL2024

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs

Jintian Zhang, Cheng Peng, Mengshu Sun +6

Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in…

cs.AI2024★ 1 cited

OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System

Ningyu Zhang, Zekun Xi, Yujie Luo +11

Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs…

cs.LG2024

Personalized Binomial DAGs Learning with Network Structured Covariates

Boxin Zhao, Weishi Wang, Dingyuan Zhu +5

The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…

cs.CL2024★ 1 cited

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs

Junjie Wang, Mingyang Chen, Binbin Hu +10

Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enha…

cs.LG2024★ 1 cited

Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

Chunjing Gan, Dan Yang, Binbin Hu +9

In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucinati…

cs.LG2024

Your decision path does matter in pre-training industrial recommenders with multi-source behaviors

Chunjing Gan, Binbin Hu, Bo Huang +5

Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are…

cs.LG2024★ 1 cited

HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning

Rong Han, Wenbing Huang, Lingxiao Luo +5

Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for str…

cs.IR2024★ 1 cited

Can Small Language Models be Good Reasoners for Sequential Recommendation?

Yuling Wang, Changxin Tian, Binbin Hu +6

Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are s…

cs.CL2024★ 1 cited

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models

Jun Xu, Mengshu Sun, Zhiqiang Zhang +1

Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction,…

cs.CL2024★ 21 cited

A Comprehensive Study of Knowledge Editing for Large Language Models

Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19

Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…