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Found 9 papers, 6 papers with code
Knowledge Mechanisms in Large Language Models: A Survey and Perspective
Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI.
Knowledge Circuits in Pretrained Transformers
To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head.
WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge.
Detoxifying Large Language Models via Knowledge Editing
This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs).
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).
A Comprehensive Study of Knowledge Editing for Large Language Models
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on
knowledge editing
on zsRE
(using extra training data)
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
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cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
A Deep Retinal Image Quality Assessment Network with Salient Structure Priors
Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures.
A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment
Retinal image quality assessment is an essential task in the diagnosis of retinal diseases.
