AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs

With the rapid advancement of Multi-modal Large Language Models (MLLMs), several diagnostic benchmarks have recently been developed to assess these models' multimodal reasoning proficiency. However, these benchmarks are restricted to assessing primarily the visual aspect and do not examine the holistic audio-visual (AV) understanding. Moreover, currently, there are no benchmarks that investigate the capabilities of AVLLMs to calibrate their responses when presented with perturbed inputs. To this end, we introduce Audio-Visual Trustworthiness assessment Benchmark (AVTRUSTBENCH), comprising 600K samples spanning over 9 meticulously crafted tasks, evaluating the capabilities of AVLLMs across three distinct dimensions: Adversarial Attack, Compositional Reasoning, and Modality-specific Dependency. Using our benchmark, we extensively evaluate 16 state-of-the-art AVLLMs. The findings reveal that the majority of existing models fall significantly short of achieving human-like comprehension, offering valuable insights for future research directions. To alleviate the limitations in the existing approaches, we further propose a robust, modelagnostic calibrated audio-visual preference optimizationbased training strategy CAVPref, obtaining a gain up to 30.19% across all 9 tasks.