Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective (2024)

Abstract: Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from over-reliance on unimodal biases (e.g., language bias and vision bias), leading to incorrect answers in complex multimodal tasks. To investigate this issue, we propose a causal framework to interpret the biases in Visual Question Answering (VQA) problems. Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions. Based on the analysis, we introduce 1) a novel MORE dataset with 12,000 challenging VQA instances requiring multi-hop reasoning and overcoming unimodal biases. 2) a causality-enhanced agent framework CAVE that guides models to comprehensively integrate information from different modalities and mitigate biases. Our experiments show that MLLMs perform poorly on MORE, indicating strong unimodal biases and limited semantic understanding. However, when integrated with our CAVE, promising improvements in reasoning and bias mitigation can be seen. These findings provide important insights for the development of more robust MLLMs and contribute to the broader goal of advancing multimodal AI systems capable of deeper understanding and reasoning. Our project page is at https://github.com/OpenCausaLab/MORE.

## introduction following the success of large language models (llms) @xcite , multimodal llms (mllms) @xcite have been proposed for various vision-language tasks @xcite . despite their promising results, it remains unclear if they truly understand images and text in the context of multimodal reasoning. as shown in the knowledge-based visual question answering (vqa) problems of figure 1 , when prompted with "which country is hosting the next world cup after this venue?" mllms such as @xcite and llava @xcite may capture the language bias of "the next world cup" and think that the next world cup will be "the 2022 fifa world cup held in qatar" (which is also outdated knowledge), while ignoring the exact venue presented in the image. similarly, when presented with an image of "the shard" in london, mllm directly identifies "the representative building is the shard" influenced by vision bias, overlooking the specific constraint "in berlin" mentioned in the question. these inherent issues pose significant challenges to the reasoning capabilities of mllms, particularly when faced with more complex questions. to investigate the issue of mllms' over-reliance (more) on such unimodal biases, we propose a causal framework to interpret and quantify language and vision biases. we begin by defining a datasets knowledgebased multi-hop reasoning answer type unimodal biases evaluation rationale # size visual7w @xcite ) ✗ ✗ open-ended ✗ ✗ 327.9k vqa (v2) @xcite ✗ ✗ open-ended ✗ ✗ 1.1m fvqa @xcite ) ✓ ✗ open-ended ✗ ✓ 5.8k okvqa @xcite ) ✓ ✗ open-ended ✗ ✗ 14k s3vqa @xcite ) ✓ ✗ open-ended ✗ ✗ 7.5k a-okvqa @xcite ✓ ✗ multi-choice ✗ ✓ 23.7k infoseek @xcite ) based on the above causal analysis, we curate a novel dataset termed more, comprising 12,000 vqa instances. this dataset advances existing vqa datasets by introducing a dedicated evaluation of unimodal biases. we adopt a multiple choice question (mcq) format to facilitate the evaluation, where each instance consists of an image, a question, and four candidate options. the image is sourced from an existing vqa dataset @xcite . for question and option curation, we incorporate a knowledge graph (kg) @xcite , allowing us to simulate mllms to navigate potential paths within the causal graph. specifically, the options consist of one correct answer, and three distractors targeting language bias, vision bias, and multi-hop reasoning, respectively. we also provide the reasoning path, designated as causal rationale, in the kg for each instance, offering interpretability for evaluation. as summarized in through extensive experiments on more with several leading mllms, we observe that: 1) most mllms perform much poorly on more, showing strong reliance on unimodal biases and low robustness to disturbances; 2) mllms still struggle to achieve precise semantic understanding in multimodal reasoning; 3) with our propose method cave, we can mitigate the unimodal bias in mllms, yet it still falls short of the ideal. this indicates that addressing the unimodal bias in mllms is a highly challenging issue that merits further exploration. overall, our main contributions are as follows: ## a causal framework inspired by @xcite , we introduce a causal graph for mllm predictions on vqa problems, highlighting language and vision biases and assessing their causal effects via controlled interventions @xcite . ## the more dataset from section 3.1 to 3.3, we construct the more dataset that exploits the unimodal biases defined in section 2.2 and requires multi-hop reasoning. in section 3.4, we discuss how to quantify the causal effects of images and questions (defined in section 2.3) using our constructed dataset. ## cave for bias mitigation to mitigate unimodal biases in mllms and improve their reasoning capabilities, we propose cave, a causality-enhanced agent framework in this section. for a given instance, cave starts by using a question decomposer to break down complex questions into simpler, step-by-step subquestions, explicitly avoiding spurious paths that may lead to incorrect answers. for each subquestion, cave uses a causality-enhanced reasoner to evaluate the correctness of the decomposed subquestion. specifically, based on the aforementioned causal analysis in section 2, it assesses whether the decomposed subquestion changes under two conditions: 1) rephrasing the question text, or 2) replacing the image with another captured by the same entity from a different angle or at a different time. if it identifies that the decomposed subquestion needs to be altered, this indicates that the previous understanding is influenced by irrelevant factors or biases, failing to capture the true semantics. in such cases, it will enforce a new round of question decomposition until it determines that the current subquestions are appropriate and accurate. next, cave uses a verifier to strategically employ external tools and evaluate their outputs, acquiring the necessary context or information, such as image and text retrieval, to provide a precise answer. this iterative process of answering and verifying continues until all subquestions are resolved. by incorporating external knowledge, the final verified output integrates information from the entire reasoning process to provide a correct answer. a detailed illustration of cave and the prompt template is provided in appendix e. ## related work multimodal large language models (mllms) recent advances in large language models (llms) have led to the emergence of mllms, which demonstrate exceptional performance in multimodal tasks @xcite @xcite @xcite . however, in contrast to the extensive evaluation of reasoning capabilities in llms @xcite , currently, the evaluation of mllms primarily emphasizes basic visual tasks @xcite @xcite , with limited investigation into their reasoning capabilities. knowledge-based vqa datasets existing vqa datasets @xcite @xcite are limited by their focus on image-related information, lack of multi-hop reasoning, open-ended answers, and reasoning rationales. they also fail to measure the effect of language and vision biases. our more dataset, outlined in language and vision biases in vqa research reveals that some vqa models primarily depend on statistical priors from training data instead of genuinely comprehending image content @xcite . these models exhibit language and vision biases; the former arises from strong correlations between specific questions and answers @xcite , and the latter from frequent co-occurrences of textual and visual elements in the dataset @xcite . recent efforts to address these biases mostly involve data augmentation @xcite . besides, several studies @xcite @xcite use examples derived from language biases to assess object hallucination in models. however, these constructed examples are typically counterfactual, with the answer types limited to yes/no, essentially judging whether the constructed statements are correct or incorrect. ## conclusion this paper presents a comprehensive approach to quantifying and mitigating the unimodal biases in mllms. through our causal inference framework, we provide an in-depth analysis to assess the causal effects of such biases on the model's prediction in vqa problems. the introduced more dataset challenges mllms to engage in multi-hop reasoning and to overcome language and vision biases, thereby pushing the boundaries of their reasoning capabilities. our proposed cave method demonstrates significant potential in enhancing the reasoning abilities of mllms.