VLind-Bench: Measuring Language Priors in Large Vision-Language Models

The paper presents VLind-Bench, a new benchmark for assessing language priors in Large Vision-Language Models, demonstrating that these models often rely heavily on language cues and that this reliance decreases with model size, with RLHF techniques potentially reducing this bias.

Abstract

Large Vision-Language Models (LVLMs) have demonstrated outstanding performance across various multimodal tasks. However, they suffer from a problem known as language prior, where responses are generated based solely on textual patterns while disregarding image information. Addressing the issue of language prior is crucial, as it can lead to undesirable biases or hallucinations when dealing with images that are out of training distribution. Despite its importance, current methods for accurately measuring language priors in LVLMs are poorly studied. Although existing benchmarks based on counterfactual or out-of-distribution images can partially be used to measure language priors, they fail to disentangle language priors from other confounding factors. To this end, we propose a new benchmark called VLind-Bench, which is the first benchmark specifically designed to measure the language priors, or blindness, of LVLMs. It not only includes tests on counterfactual images to assess language priors but also involves a series of tests to evaluate more basic capabilities such as commonsense knowledge, visual perception, and commonsense biases. For each instance in our benchmark, we ensure that all these basic tests are passed before evaluating the language priors, thereby minimizing the influence of other factors on the assessment. The evaluation and analysis of recent LVLMs in our benchmark reveal that almost all models exhibit a significant reliance on language priors, presenting a strong challenge in the field.

Method

The authors used a multi-faceted methodology to create VLind-Bench, which includes four types of assessments to test different capabilities of LVLMs: commonsense knowledge, visual perception, commonsense bias, and language prior. They generated counterfactual textual contexts and corresponding true and false statements using GPT-4, and then created multiple images per test using DALL-E 3 to ensure a diverse range of image styles (photorealistic, illustration, and cartoon) for the language prior test. To verify the quality of the generated data, they employed a rigorous human verification process involving three graduate students. The final dataset included 302 instance triples and 2,576 images. They evaluated the performance of various LVLMs on this benchmark and analyzed the results to draw conclusions about the models' reliance on language priors and the impact of model scale and training methodologies.

Main Finding

The authors discovered that almost all Large Vision-Language Models (LVLMs) evaluated exhibit a significant reliance on language priors, which presents a substantial challenge in the field. They found that the reliance on language priors is inversely proportional to the scale of the backbone Large Language Model (LLM), suggesting that larger models are less prone to overfitting to the dataset during visual instruction tuning and better maintain their ability to attend to image information. Additionally, they observed that models trained with Reinforcement Learning from Human Feedback (RLHF) techniques demonstrate superior performance compared to models of similar or greater scale, indicating that these methods can significantly aid in reducing the reliance on language priors. The study also revealed that model performance varies significantly depending on the concept being tested, with some models scoring zero in language prior tests for certain concepts, indicating a lack of robust understanding of physical properties in counterfactual situations.

Conclusion

The conclusion of the paper is that VLind-Bench, the proposed benchmark, is effective in precisely measuring language priors in LVLMs and diagnosing their capabilities in multiple aspects. The authors advocate for a pipelined evaluation paradigm for constructing benchmarks to disentangle specific abilities intended for measurement. They note that while the reliance on language priors is generally high across models, it is more pronounced in open-source models and those with smaller backbone LLMs. The application of RLHF techniques shows promise in reducing this reliance. The authors acknowledge limitations such as potential unconsidered factors in assessing language priors and the need for further research on methods for feeding text-only inputs to LVLMs. They suggest future work could explore the use of automatically generated training data to mitigate reliance on language priors.

Keywords

Large Vision-Language Models, LVLMs, language priors, benchmark, VLind-Bench, commonsense knowledge, visual perception, commonsense biases, language prior, model scale, Reinforcement Learning from Human Feedback, RLHF, DALL-E 3, GPT-4, data generation, human verification, model performance, concept variability, future work, training data, mitigation.