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20242026
most citedIEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

7 citations · 15 across the 11 of their papers we have counts for

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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.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.CL2024

RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment

Xiaohan Wang, Xiaoyan Yang, Yuqi Zhu +7

Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks. However, they still face…

cs.CL2024

Editing Conceptual Knowledge for Large Language Models

Xiaohan Wang, Shengyu Mao, Ningyu Zhang +6

Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, whi…

cs.CL2024★ 4 cited

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents

Yuqi Zhu, Shuofei Qiao, Yixin Ou +7

Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interact…

cs.CL2024★ 7 cited

IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

Honghao Gui, Lin Yuan, Hongbin Ye +4

Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that…

cs.CL2024★ 3 cited

Unified Hallucination Detection for Multimodal Large Language Models

Xiang Chen, Chenxi Wang, Yida Xue +7

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such halluci…