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Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective


Core Concepts
MLLMs often suffer from unimodal biases, impacting reasoning capabilities.
Abstract
The article discusses the challenges posed by unimodal biases in Multimodal Large Language Models (MLLMs). It introduces a causal framework to interpret and quantify language and vision biases in Visual Question Answering (VQA) problems. The authors propose the MORE dataset to challenge MLLMs' reasoning abilities, offering insights for future research. Strategies to mitigate biases include the DeVA framework and fine-tuning LLaVA models. Experimental results and analyses are provided to evaluate the effectiveness of these strategies. Directory: Introduction Recent advancements in Large Language Models (LLMs) have led to the development of Multimodal LLMs (MLLMs). Unimodal Biases Language and vision biases impact MLLMs' reasoning capabilities. Causal Framework Proposes a framework to interpret and quantify biases in VQA problems. MORE Dataset Challenges MLLMs to overcome biases and enhance reasoning abilities. Mitigating Biases Strategies include the DeVA framework and fine-tuning LLaVA models. Experimental Results Evaluation of MLLMs on different datasets after applying mitigation strategies.
Stats
The MORE dataset consists of 12,000 VQA instances. GPT-4V performs best under the "Open-ended" setting. Gemini Pro exceeds the random baseline at 28.9% accuracy. LLaVA shows significant improvement after fine-tuning on the MORE dataset.
Quotes
"The model may attend directly to the image I via a causal path I →A, leading to the emergence of vision bias." "The model may directly process the question Q in two ways: by focusing on the core semantics S via the causal path Q →S →A, or on the irrelevant part T via the causal path Q → T →A."

Deeper Inquiries

How can the DeVA framework be further optimized to enhance MLLMs' reasoning abilities?

The DeVA framework can be further optimized by incorporating more sophisticated verification mechanisms to ensure the accuracy of the answers provided by MLLMs. This can involve integrating external knowledge sources dynamically during the verification process to validate the reasoning steps taken by the model. Additionally, refining the question decomposition strategy to generate more granular subquestions that progressively lead to the final answer can enhance the model's reasoning capabilities. Implementing a feedback loop mechanism that iteratively refines the reasoning process based on verification outcomes can also contribute to improving the overall performance of MLLMs within the DeVA framework.

What are the potential implications of unimodal biases in MLLMs for real-world applications?

Unimodal biases in MLLMs can have significant implications for real-world applications, especially in scenarios where accurate and unbiased decision-making is crucial. In applications such as medical diagnosis, financial forecasting, or autonomous driving, relying on MLLMs with unimodal biases can lead to incorrect or biased outcomes. For instance, in healthcare, if a medical diagnosis MLLM exhibits language bias and overlooks critical visual cues in medical images, it could result in misdiagnosis and inappropriate treatment recommendations. Similarly, in financial forecasting, vision bias in MLLMs analyzing market trends could lead to inaccurate predictions and financial losses. These biases can undermine the reliability and trustworthiness of AI systems in critical decision-making processes, highlighting the importance of mitigating unimodal biases in MLLMs for real-world applications.

How can the findings of this study be applied to improve the performance of other AI models beyond VQA tasks?

The findings of this study can be applied to enhance the performance of other AI models beyond VQA tasks by addressing biases and improving reasoning capabilities. Here are some ways these findings can be leveraged: Bias Mitigation: The strategies proposed in this study to mitigate unimodal biases, such as the DeVA framework and fine-tuning with causal rationales, can be adapted for other AI models. By identifying and addressing biases in different modalities, such as text or audio data, AI models can make more accurate and unbiased predictions. Causal Reasoning: The causal framework introduced in this study can be applied to various AI tasks that require multi-modal reasoning. By analyzing the causal effects of different factors on model predictions, AI models can improve their understanding of complex relationships and make more informed decisions. External Knowledge Integration: Incorporating external knowledge sources, as done in the DeVA framework, can enhance the contextual understanding of AI models across different tasks. By leveraging external information dynamically during the reasoning process, AI models can improve their performance in diverse applications. Interpretability: Generating causal rationales for model predictions, as demonstrated in this study, can enhance the interpretability of AI models. By providing explanations for their decisions, AI models can build trust with users and stakeholders in various domains, including healthcare, finance, and natural language processing. By applying the insights and methodologies from this study to a broader range of AI models, researchers and practitioners can advance the capabilities and reliability of AI systems in various real-world applications.
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