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

21 citations · 38 across the 9 of their papers we have counts for

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Showing 2024 · cs.CLShow all

5 papers · 2 filters

cs.CL2024

Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process

Peng Wang, Xiaobin Wang, Chao Lou +3

In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the r…

cs.CL2024★ 1 cited

Knowledge Mechanisms in Large Language Models: A Survey and Perspective

Mengru Wang, Yunzhi Yao, Ziwen Xu +10

Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI. This paper reviews knowledge mechanism analysis from a novel ta…

cs.CL2024★ 1 cited

RaFe: Ranking Feedback Improves Query Rewriting for RAG

Shengyu Mao, Yong Jiang, Boli Chen +7

As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream…

cs.CL2024★ 4 cited

WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

Peng Wang, Zexi Li, Ningyu Zhang +6

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing.…

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…