Evaluating Knowledge Retention in Multimodal Models

With the rise of advanced language models like GPT-4, there is growing interest in models that can handle both text and images, known as multimodal large language models (MLLMs). These models aim to combine the skills of language and vision by Fine-tuning existing models on new tasks. However, one significant issue that remains is Catastrophic Forgetting. This happens when a model loses its ability to perform previous tasks after being trained on new data.

#The Problem of Catastrophic Forgetting

Catastrophic forgetting occurs when a model focuses too much on new data and forgets what it learned before. In the context of MLLMs, this means that after being fine-tuned on specific tasks, the models cannot perform as well on general tasks they were initially trained for. This problem has been studied in traditional machine learning but less so in the area of MLLMs.

#The Evaluating Mul Timodality (EMT) Framework

To address this issue, a new framework called Evaluating Mul Timodality (EMT) was introduced. This framework evaluates how well MLLMs maintain their ability to classify images after being fine-tuned with text and image data. It treats MLLMs as if they were Image Classifiers, asking them to identify objects in images and comparing their performance to when they were first trained.

#Evaluation Process

The evaluation process involves several steps:

1. An image is selected from a dataset.
2. The MLLM is prompted to classify the image.
3. The outputs from the MLLM are checked for accuracy against known labels using another language model.

Through this method, researchers can determine how much the MLLMs have retained their original capabilities after fine-tuning.

#Initial Findings

Initial tests using the EMT framework showed that most fine-tuned MLLMs did not perform as well in classifying images compared to their earlier capabilities. They often produced lower accuracy in recognizing objects in images that they had not specifically fine-tuned on. This indicates a pattern of catastrophic forgetting across different models.

#Fine-Tuning and Its Effects

Further experiments were conducted by fine-tuning a popular MLLM. Interestingly, they found that some initial fine-tuning could improve performance on similar tasks. However, as training continued, the model began to generate irrelevant or incorrect outputs, a phenomenon known as hallucination. This suggests a delicate balance where too much fine-tuning could lead to forgetting prior knowledge.

#Moderate Fine-Tuning is Beneficial

Moderate fine-tuning on similar datasets initially showed improvements in the model's performance. This suggests that correctly aligning the features of text and images can help the model retain its original capabilities. However, if fine-tuning is excessive, the model struggles to recall earlier learned tasks and begins to produce inaccurate responses.

#Assessing Performance Degradation

When evaluating the performance of various MLLMs, the researchers identified three main issues that contribute to degrading performance:

1. Incorrect Predictions: Sometimes, models simply misclassify objects in images.
2. Intrinsic Hallucination: This happens when the model creates outputs that directly contradict the input it receives.
3. Extrinsic Hallucination: Here, the model produces unrelated or unverifiable information that does not connect to the input.

These issues highlight the challenges MLLMs face when they become too focused on new input data and start to forget their original training.

#Comparison of MLLMs

Different MLLMs were compared to see how they reacted to fine-tuning stages. Some models performed better than others, revealing that the specific training methods used can greatly influence the outcomes. For example, one model slightly outperformed its foundational vision model, while others struggled to keep up with their initial abilities.

#Importance of Diverse Datasets

The findings suggested that having a more diverse fine-tuning dataset is crucial. Models trained on a variety of tasks and inputs were less likely to suffer from catastrophic forgetting. In contrast, training on a single data type or limited set led to a more dramatic decline in performance across different tasks.

#Future Research Directions

The research points to many opportunities for future work. Investigating how to reduce biased outputs, improve generalization abilities, and further understand Hallucinations in outputs are vital next steps. Moreover, applying the findings from this study to other scenarios, such as reasoning tasks or visual perception challenges, could also be beneficial.

#Conclusion

The introduction of the EMT framework presents a new way to evaluate MLLMs, focusing on their ability to retain knowledge from their foundational training. The findings highlight the challenges posed by catastrophic forgetting and demonstrate the importance of moderate fine-tuning. A balance must be struck to ensure MLLMs maintain their prior knowledge while adapting to new tasks. Further efforts in research will help to mitigate these issues and improve the overall performance of multimodal language models.

#Related Works

#Fine-Tuning and Catastrophic Forgetting

Fine-tuning models has changed how we approach natural language processing, but it still faces significant challenges, particularly catastrophic forgetting. Many methods have been proposed to combat this issue, such as training regularizations and adjusting learning rates. However, in the context of MLLMs, the effects of fine-tuning on performance are still being explored.

#Multimodal Large Language Models

MLLMs have revolutionized how we think about combining text and image processing. These models work by interpreting multiple forms of information to complete complex tasks. Recent advancements have focused on improving the reasoning capabilities of these models, allowing them to perform tasks that require a better understanding of context.

#Neural Collapse and Minority Collapse

Recent theories have proposed concepts like neural collapse, which looks at how classifiers behave when minimizing loss in balanced datasets. In contrast, minority collapse examines how classifiers can struggle with imbalanced data, leading to performance drops. These theoretical frameworks provide useful insights into catastrophic forgetting in MLLMs, especially when certain classes are underrepresented during training.

#Experimental Setup

#Training with ResNet

For the experiment, the researchers started by training an image classification model using a popular architecture called ResNet. The model was pre-trained using a set of classes before being fine-tuned. The results confirmed that fine-tuning on a smaller set of classes often leads to significant forgetting of the larger set of classes.

#Fine-Tuning with CLIP

The Contrastive Language-Image Pre-training (CLIP) model was also fine-tuned to see if similar forgetting occurred. The experiments showed that after fine-tuning, performance on other datasets dropped significantly, reinforcing the idea that MLLMs are vulnerable to loss of knowledge after training.

#Implications for Future Models

The insights gained from this research can lead to better training methods for MLLMs, ensuring they retain essential capabilities even after fine-tuning. Future models should focus more on balancing training datasets to prevent issues related to catastrophic forgetting.

#Conclusion and Next Steps

In summary, the study of catastrophic forgetting in MLLMs has revealed significant insights. By using the EMT framework, researchers can better understand how fine-tuning impacts model performance and knowledge retention. Further research is needed to refine training techniques and enhance the versatility of these advanced models, ensuring they perform well across a wide range of tasks.