Addressing Spurious Bias in Multimodal Models

In recent years, large language models have made impressive advancements in understanding language and images together. These models, known as multimodal large language models (MLLMs), combine both language and vision capabilities to answer questions about images or perform tasks that require both types of information. However, there's a hidden problem that can make these models less reliable: they sometimes rely on misleading hints in the data that can steer them wrong. This issue is known as spurious bias, and it can lead to incorrect or unreliable predictions.

#What is Spurious Bias?

Spurious bias occurs when a model learns to make predictions based on connections that are not genuinely relevant to the task at hand. For example, suppose a model is shown images of shoes with a specific background repeatedly. If it learns to associate the background with the shoes, it might incorrectly identify a shoe based solely on the background instead of the shoe itself. This happens because the model is not focusing on the actual objects but rather on the misleading hints surrounding them.

In the realm of multimodal models, spurious biases can arise when the connection between visual elements and textual descriptions becomes unreliable. For instance, if a model is trained on certain images and learns that a specific label or word often describes an object in those images, it might wrongly assume that this label applies to a new image just because it shares similar context or background, even if the object is different.

#The Problem with MLLMs

Despite their advancements, MLLMs have not yet overcome the challenges posed by spurious biases. This issue is critical because it affects their performance and reliability in real-world applications. To ensure that models can accurately understand and generate responses based on images and text, it is crucial to recognize and address spurious biases.

Many studies have focused on single-modality models, which look at either language or vision independently. However, MLLMs need to be evaluated in a way that takes into account the unique challenges posed by blending both modalities. This is a relatively unexplored area, and most current MLLMs might still struggle with spurious biases when faced with complex visual inputs.

#Introducing MM-SpuBench

To better evaluate and understand how spurious biases affect MLLMs, a new benchmark called MM-SpuBench has been created. This benchmark serves as a tool to assess MLLMs' reliance on misleading connections in visual and textual data. It focuses on Visual Question Answering (VQA), a task where a model must answer questions about images.

MM-SpuBench asks models to respond to questions that deliberately test their understanding of images without leading them astray by using misleading cues. By doing this, researchers can identify which types of spurious biases are most prevalent and how severely they impact the models' performances.

#How MM-SpuBench Works

The MM-SpuBench evaluates spurious biases using a set of carefully constructed questions based on images from various sources. These questions are designed to expose the models' reliance on spurious correlations. The process involves multiple steps:

1. Image Selection: Images are chosen from various datasets, ensuring a wide range of visual content. Pre-selected images help identify cases where models might rely on misleading hints.

2. Attribute Identification: For each image, core attributes (essential features) and spurious attributes (misleading features) are identified. Using advanced models, researchers can extract these features, which are essential for constructing well-informed questions that test the models.

3. VQA Generation: Based on the identified attributes, questions are crafted to see if the models can correctly identify the core object without being misled by spurious information. Each question includes multiple choice answers, some of which are designed to confuse the model.

By analyzing models' responses to these questions, researchers can determine how well they manage to distinguish between core and spurious information, shedding light on their reliability and robustness.

#Investigating Current MLLMs

Using MM-SpuBench, researchers have evaluated a range of today's popular MLLMs to see how they respond to questions that test their understanding of images. The findings reveal a mixed picture:

• Close-Sourced Models: These proprietary models tend to perform better, suggesting that they might have more advanced techniques to deal with spurious bias.
• Open-Sourced Models: These models show varying degrees of success, often struggling more than their close-sourced counterparts. This could be due to differences in training data or architecture.

The results indicate that while some models perform well in detecting misleading cues, others significantly struggle, especially in cases where the spurious attributes are more complex or less obvious.

#Types of Spurious Biases

MM-SpuBench identifies nine distinct types of spurious biases to systematically evaluate MLLMs. Here are some of them:

• Background Bias: This occurs when a model uses the background of an image to make decisions. If an object is often set against the same background, the model might incorrectly associate the background with the object itself.

• Color Bias: This happens when the model learns to associate colors with specific objects, leading it to misidentify objects based solely on color similarities.

• Size and Proximity Bias: Models may mistakenly assume that objects that are larger or closer in a scene are more important, leading to inaccurate conclusions.

• Attribute Confusion: Misleading attributes, such as texture or shape that are not core to the object's identity, can skew the model's understanding.

Each of these biases can lead to incorrect responses and highlight the need for better alignment techniques between visual and language information.

#Results from the Benchmark

The evaluation using MM-SpuBench showed notable performance gaps between different types of MLLMs. By comparing the accuracy of their responses to the constructed questions, researchers revealed several important insights:

• Close-Sourced Models: These models generally showed higher accuracy, particularly with spurious biases related to backgrounds and colors, indicating that they have mechanisms in place to manage these common issues.

• Open-Sourced Models: On the other hand, many open-sourced models performed poorly with biases related to size and perspective, suggesting that they might not be engineered to handle these complexities effectively.

#Implications for Future Research

The findings from using MM-SpuBench highlight the importance of addressing spurious biases in MLLMs. There are several key implications for future research:

1. Enhanced Design of MLLMs: Insights gained from analyzing spurious biases can guide the design of new models, leading to structures that are more robust against misleading correlations.

2. Improved Training Techniques: Training methods should prioritize identifying and correcting spurious biases, ensuring that models learn to focus on core attributes rather than distractions.

3. Benchmarking Practices: MM-SpuBench sets a new standard for evaluating MLLMs by focusing on realistic scenarios and common biases. This can inspire future research to create similar or more refined benchmarks.

4. Broader Applications: By developing more reliable models, applications in fields such as healthcare, education, and automated systems can benefit from increased robustness and trustworthiness.

#Conclusion

As multimodal large language models continue to advance, understanding and addressing spurious biases will be crucial. The introduction of MM-SpuBench provides a valuable tool for researchers to test and improve these models, helping them to become more reliable in real-world situations. By focusing on identifying and correcting misleading correlations, future MLLMs may achieve greater performance and reliability, ultimately enhancing their effectiveness in various applications. The journey towards better multimodal understanding is ongoing, and with tools like MM-SpuBench, there is hope for more robust and trustworthy AI systems.