Can We Edit Multimodal Large Language Models?

• URL: http://arxiv.org/abs/2310.08475v5
• Date: Thu, 18 Apr 2024 15:46:22 GMT
• Title: Can We Edit Multimodal Large Language Models?
• Authors: Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, Ningyu Zhang,
• Abstract summary: We construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components.
• Score: 38.292574747632635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models [57.091523832149655]
  We propose a mixture of multimodal experts (MoME) to mitigate task interference and obtain a generalist MLLM. Our MoME is composed of two key components, a mixture of vision experts (MoVE) and a mixture of language experts (MoLE)
  arXiv  Detail & Related papers  (2024-07-17T16:31:38Z)
• Editing Conceptual Knowledge for Large Language Models [65.38231526537476]
  This paper pioneers the investigation of editing conceptual knowledge for Large Language Models (LLMs) We construct a novel benchmark dataset ConceptEdit and establish a suite of new metrics for evaluation. experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge.
  arXiv  Detail & Related papers  (2024-03-10T16:57:10Z)
• LLMBind: A Unified Modality-Task Integration Framework [38.95771765322677]
  We introduce textbfLLMBind, a novel framework designed to unify a diverse array of multi-modal tasks. By harnessing a Mixture-of-Experts (MoE) Large Language Model (LLM), LLMBind processes multi-modal inputs and generates task-specific tokens, enabling the invocation of corresponding models to accomplish tasks.
  arXiv  Detail & Related papers  (2024-02-22T12:36:31Z)
• The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse [58.0132400208411]
  Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. benchmarking Large Language Models after each edit is impractically time-consuming and resource-intensive. We have utilized GPT-3.5 to develop a new dataset, HardEdit, based on hard cases.
  arXiv  Detail & Related papers  (2024-02-15T01:50:38Z)
• MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA [34.21194537887934]
  We propose a plug-in Model Editing method based on neuron-indexed dynamic LoRA (MELO) Our proposed MELO achieves state-of-the-art editing performance on three sequential editing tasks.
  arXiv  Detail & Related papers  (2023-12-19T02:11:01Z)
• Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration [50.94902442781148]
  We propose a novel multi-modal large language model (LLM) that seamlessly integrates visual, audio, and textual information. Macaw-LLM consists of three main components: a modality module for encoding multi-modal data, a cognitive module for harnessing pretrained LLMs, and an alignment module for harmonizing diverse representations. We construct a large-scale multi-modal instruction dataset in terms of multi-turn dialogue, including 69K image instances and 50K video instances.
  arXiv  Detail & Related papers  (2023-06-15T12:45:25Z)
• Editing Large Language Models: Problems, Methods, and Opportunities [51.903537096207]
  This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. We provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. 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.
  arXiv  Detail & Related papers  (2023-05-22T16:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.