测量与改进视觉语言模型中的思维链推理
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
AI总结：
本文提出基于思维链的一致性测量方法和CURE基准以评估视觉语言模型的推理性能与一致性，并设计两阶段训练框架（监督微调与LLM反馈）有效提升了模型的推理能力。
摘要：
视觉语言模型（VLMs）近期作为视觉助手展现了强大的效能，能够解析关于视觉内容的自然查询并生成类人的输出。在本研究中，我们探索了这些模型基于感知信息展现类人推理的能力。为了解决关于其推理能力在多大程度上完全一致且有依据的关键问题，我们还测量了这些模型的推理一致性。我们通过提出一种基于思维链（CoT）的一致性测量方法来实现这一点。然而，这种评估需要一个包含高层推理和详细推理链的基准，其成本很高。我们通过提出一个LLM-Human-in-the-Loop流水线来应对这一挑战，该流水线显著降低了成本，同时确保了高质量数据集的生成。基于该流水线和现有的粗粒度标注数据集，我们构建了CURE基准，以测量VLMs的零样本推理性能和一致性。我们评估了现有的最先进VLMs，发现即使是表现最好的模型也无法展现出强大的视觉推理能力和一致性，这表明要让VLMs像人类一样系统且一致地进行视觉推理，仍需大量努力。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，我们通过结合LLMs提供的反馈来进一步增强训练过程，以产生高度一致且有依据的推理链。我们实证突出了我们的框架在推理性能和一致性方面的有效性。
英文摘要：
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.
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