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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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Showing 2024 · cs.CLShow all

5 papers · 2 filters

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.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.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…