通过等效与互补采样，评估多模态大模型预测可信度。
FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs
- 设计等效与互补输入采样，探测模型一致性与敏感性。
- 在视觉和音频任务上，选择性预测性能提升33.3%和29.6%。
- 无需真值标签，仅需黑盒访问，适用于各类现成多模态模型。
多模态大语言模型（MLLMs）的可信度评估因多模态输入形式多样而具有挑战性。本文提出功能等效采样信任评估方法（FESTA），一种基于等效与互补输入采样的多模态输入采样技术，用于生成不确定性度量。该任务保持型采样方法扩展输入空间，以探测模型的一致性（通过等效样本）和敏感性（通过互补样本）。FESTA仅需模型的输入输出访问（黑盒），无需真实标签（无监督）。在多种现成多模态大模型上，针对视觉和音频推理任务的实验表明，提出的不确定性估计在检测误预测方面显著提升选择性预测性能：视觉-大模型相对提升33.3%，音频-大模型相对提升29.6%（以受试者工作特征曲线下面积，AUROC为指标）。代码已开源。
原文摘要 · Abstract (English)
The accurate trust assessment of multimodal large language models (MLLMs) generated predictions, which can enable selective prediction and improve user confidence, is challenging due to the diverse multi-modal input paradigms. We propose Functionally Equivalent Sampling for Trust Assessment (FESTA), a multimodal input sampling technique for MLLMs, that generates an uncertainty measure based on the equivalent and complementary input samplings. The proposed task-preserving sampling approach for uncertainty quantification expands the input space to probe the consistency (through equivalent samples) and sensitivity (through complementary samples) of the model. FESTA uses only input-output access of the model (black-box), and does not require ground truth (unsupervised). The experiments are conducted with various off-the-shelf multi-modal LLMs, on both visual and audio reasoning tasks. The proposed FESTA uncertainty estimate achieves significant improvement (33.3% relative improvement for vision-LLMs and 29.6% relative improvement for audio-LLMs) in selective prediction performance, based on area-under-receiver-operating-characteristic curve (AUROC) metric in detecting mispredictions. The code implementation is open-sourced.
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