FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs

• URL: http://arxiv.org/abs/2509.16648v3
• Date: Sun, 02 Nov 2025 05:16:52 GMT
• Title: FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs
• Authors: Debarpan Bhattacharya, Apoorva Kulkarni, Sriram Ganapathy,
• Abstract summary: multimodal large language models (MLLMs) generated predictions can enable selective prediction and improve user confidence.<n>We propose Functionally Equivalent Sampling for Trust Assessment (FESTA), a multimodal input sampling technique for MLLMs.<n>FESTA generates an uncertainty measure based on the equivalent and complementary input samplings.
• Score: 20.08099668437471
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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