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Found 11 papers, 10 papers with code
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood.
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Knowledge representation has been a central aim of AI since its inception.
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material.
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance.
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).
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)
Can We Edit Multimodal Large Language Models?
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
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cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
Editing Large Language Models: Problems, Methods, and Opportunities
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
Revisiting k-NN for Fine-tuning Pre-trained Language Models
Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP).
Editing Language Model-based Knowledge Graph Embeddings
To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.
