Investigating the Catastrophic Forgetting in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2309.10313v4
• Date: Tue, 5 Dec 2023 08:59:33 GMT
• Title: Investigating the Catastrophic Forgetting in Multimodal Large Language Models
• Authors: Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, Yi Ma
• Abstract summary: We introduce EMT: evaluating MulTimodality for evaluating the catastrophic forgetting in MLLMs. Almost all evaluated MLLMs fail to retain the same performance levels as their vision encoders on standard image classification tasks. As fine-tuning proceeds, the MLLMs begin to hallucinate, resulting in a significant loss of generalizability.
• Score: 43.89009178021342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Following the success of GPT4, there has been a surge in interest in multimodal large language model (MLLM) research. This line of research focuses on developing general-purpose LLMs through fine-tuning pre-trained LLMs and vision models. However, catastrophic forgetting, a notorious phenomenon where the fine-tuned model fails to retain similar performance compared to the pre-trained model, still remains an inherent problem in multimodal LLMs (MLLM). In this paper, we introduce EMT: Evaluating MulTimodality for evaluating the catastrophic forgetting in MLLMs, by treating each MLLM as an image classifier. We first apply EMT to evaluate several open-source fine-tuned MLLMs and we discover that almost all evaluated MLLMs fail to retain the same performance levels as their vision encoders on standard image classification tasks. Moreover, we continue fine-tuning LLaVA, an MLLM and utilize EMT to assess performance throughout the fine-tuning. Interestingly, our results suggest that early-stage fine-tuning on an image dataset improves performance across other image datasets, by enhancing the alignment of text and visual features. However, as fine-tuning proceeds, the MLLMs begin to hallucinate, resulting in a significant loss of generalizability, even when the image encoder remains frozen. Our results suggest that MLLMs have yet to demonstrate performance on par with their vision models on standard image classification tasks and the current MLLM fine-tuning procedure still has room for improvement.

Related papers

• Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning [104.27224674122313]
  Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions.
  arXiv  Detail & Related papers  (2024-11-17T01:16:37Z)
• LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
  We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM. Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM. We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
  arXiv  Detail & Related papers  (2024-10-21T17:41:28Z)
• GenCeption: Evaluate Multimodal LLMs with Unlabeled Unimodal Data [3.08543976986593]
  Multimodal Large Language Models (MLLMs) are typically assessed using expensive annotated multimodal benchmarks. This paper outlines and validates GenCeption, a novel, annotation-free evaluation method. It requires only unimodal data to measure inter-modality semantic coherence and inversely assesses MLLMs' tendency to hallucinate.
  arXiv  Detail & Related papers  (2024-02-22T21:22:04Z)
• Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance [56.04768229686853]
  Large Vision-Language Models (LVLMs) tend to hallucinate non-existing objects in the images. We introduce a framework called Mitigating hallucinAtion via classifieR-Free guIdaNcE (MARINE) MARINE is both training-free and API-free, and can effectively and efficiently reduce object hallucinations during the generation process.
  arXiv  Detail & Related papers  (2024-02-13T18:59:05Z)
• The Instinctive Bias: Spurious Images lead to Illusion in MLLMs [34.91795817316696]
  We identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers. We propose CorrelationQA, the first benchmark that assesses the visual illusion level given spurious images. We conduct a thorough analysis on 9 mainstream MLLMs, illustrating that they universally suffer from this instinctive bias to varying degrees.
  arXiv  Detail & Related papers  (2024-02-06T06:48:46Z)
• Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling [0.0]
  Recent decoder-only large language models (LLMs) perform on par with smaller state-based encoders. We explore techniques for improving the SL performance of open LLMs on IE tasks by applying layer-wise removal of the causal mask. Our findings hold for diverse SL tasks, demonstrating that open LLMs with layer-dependent CM removal outperform strong-based encoders and even instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2024-01-25T22:50:48Z)
• Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences [80.54979242912944]
  This paper introduces Mementos, a new benchmark designed to assess MLLMs' sequential image reasoning abilities. We find that MLLMs struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects.
  arXiv  Detail & Related papers  (2024-01-19T07:10:13Z)
• CLAMP: Contrastive LAnguage Model Prompt-tuning [89.96914454453791]
  We show that large language models can achieve good image classification performance when adapted this way. Our approach beats state-of-the-art mLLMs by 13% and slightly outperforms contrastive learning with a custom text model.
  arXiv  Detail & Related papers  (2023-12-04T05:13:59Z)

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.