Vision-language models (VLMs) have recently demonstrated strong efficacy as
visual assistants that can parse natural queries about the visual content and
generate human-like outputs. In this work, we explore the ability of these
models to demonstrate human-like reasoning based on the perceived information.
To address a crucial concern regarding the extent to which their reasoning
capabilities are fully consistent and grounded, we also measure the reasoning
consistency of these models. We achieve this by proposing a chain-of-thought
(CoT) based consistency measure. However, such an evaluation requires a
benchmark that encompasses both high-level inference and detailed reasoning
chains, which is costly. We tackle this challenge by proposing a
LLM-Human-in-the-Loop pipeline, which notably reduces cost while simultaneously
ensuring the generation of a high-quality dataset. Based on this pipeline and
the existing coarse-grained annotated dataset, we build the CURE benchmark to
measure both the zero-shot reasoning performance and consistency of VLMs. We
evaluate existing state-of-the-art VLMs, and find that even the best-performing
model is unable to demonstrate strong visual reasoning capabilities and
consistency, indicating that substantial efforts are required to enable VLMs to
perform visual reasoning as systematically and consistently as humans. As an
early step, we propose a two-stage training framework aimed at improving both
the reasoning performance and consistency of VLMs. The first stage involves
employing supervised fine-tuning of VLMs using step-by-step reasoning samples
automatically generated by LLMs. In the second stage, we further augment the
training process by incorporating feedback provided by LLMs to produce
reasoning chains that are highly consistent and grounded. We empirically
highlight the effectiveness of our framework in both reasoning performance and
consistency.

通过 LLM-Human-in-the-Loop 流程和 CURE 基准，我们评估了现有的 VLMs，并发现即使是表现最佳的模型也无法展示出强大的视觉推理能力和一致性，表明需要大量努力使 VLMs 能够像人类一样系统而一致地进行视觉推理。作为初步步骤，我们提出了一个两阶段训练框架，旨在提高 VLMs 的推理性能和一致性。第一阶段涉及使用由 LLMs 自动生成的逐步推理样本对 VLMs 进行监督微调。在第二阶段，我们进一步通过结合 LLMs 提供的反馈来增强训练过程，以产生高度一致和可靠的推理链。我们在推理性能和一致性方面经验上突出了我们框架的有效性。