利用接地校准多模态大语言模型的不确定性量化
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
AI总结：
本文提出利用跨模态接地与温度缩放相结合的方法来校准多模态大语言模型的不确定性量化，并在医学问答和视觉问答任务上验证了其显著提升校准效果的有效性。
摘要：
我们提出了一种针对多模态大语言模型（LLMs）量身定制的不确定性量化（UQ）校准新方法。现有的最先进UQ方法依赖于LLM在不同设置下对输入查询生成的多个响应之间的一致性。然而，这些方法在LLM持续出错的场景中往往报告更高的置信度，导致置信度相对于准确度的校准效果较差。为了解决这一问题，我们除了利用自一致性之外，还利用跨模态一致性来改善多模态模型的校准。具体来说，我们将文本响应接地到视觉输入上，并使用来自接地模型的置信度来校准整体置信度。鉴于在流程中使用接地模型会引入其自身的不确定性，我们应用温度缩放——一种被广泛接受的参数化校准技术——来校准接地模型对生成响应准确度的置信度。我们在多个多模态任务上评估了所提出的方法，例如医学问答（Slake）和视觉问答（VQAv2），并考虑了LLaVA-Med和LLaVA等多模态模型。实验表明，所提出的框架在这两个任务上都实现了显著改善的校准效果。
英文摘要：
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on consistency among multiple responses generated by the LLM on an input query under diverse settings. However, these approaches often report higher confidence in scenarios where the LLM is consistently incorrect. This leads to a poorly calibrated confidence with respect to accuracy. To address this, we leverage cross-modal consistency in addition to self-consistency to improve the calibration of the multi-modal models. Specifically, we ground the textual responses to the visual inputs. The confidence from the grounding model is used to calibrate the overall confidence. Given that using a grounding model adds its own uncertainty in the pipeline, we apply temperature scaling - a widely accepted parametric calibration technique - to calibrate the grounding model's confidence in the accuracy of generated responses. We evaluate the proposed approach across multiple multi-modal tasks, such as medical question answering (Slake) and visual question answering (VQAv2), considering multi-modal models such as LLaVA-Med and LLaVA. The experiments demonstrate that the proposed framework achieves significantly improved calibration on both tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
