REVIEW 6 cited by
Can We Edit Multimodal Large Language Models?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing process. To facilitate research in this area, we construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components for multimodal LLMs. Empirically, we notice that previous baselines can implement editing multimodal LLMs to some extent, but the effect is still barely satisfactory, indicating the potential difficulty of this task. We hope that our work can provide the NLP community with insights. Code and dataset are available in https://github.com/zjunlp/EasyEdit.
Forward citations
Cited by 6 Pith papers
-
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
DECODE identifies and separately edits modality-specific neurons in MLLMs to prevent knowledge edits from reverting under unimodal queries.
-
Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
CogEdit and MIND shift multimodal knowledge editing toward evaluating and enabling meta-cognitive skills: self-awareness, boundary monitoring, and noise robustness.
-
ScEdit: Script-based Assessment of Knowledge Editing
A script-based benchmark reveals that knowledge-editing methods perform far worse on 'How' style procedural questions than on the fact-recall questions used in standard evaluations.
-
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
REACT edits LLM facts by adding a learned belief-shift vector to hidden states only when a classifier decides the context is edit-relevant, reducing overfitting on EVOKE while keeping balanced editing metrics.
-
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
BalancEdit stores edits in a discrete codebook and sets each edit's influence radius from a rephrased positive sample and a black-image negative sample, balancing generality and locality better than prior editing base...
-
ComprehendEdit: A Comprehensive Dataset and Evaluation Framework for Multimodal Knowledge Editing
A new eight-task benchmark with in-domain metrics KGI and KPI reveals that existing multimodal editing methods degrade on related samples, and the proposed HICE method achieves a better balance.
Discussion (0). Continue with ORCID to comment.