AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs
This addresses the problem of evaluating and improving trustworthiness in AVLLMs for researchers and developers, though it is incremental as it builds on existing benchmark efforts.
The authors tackled the lack of benchmarks for assessing reliability and robustness in audio-visual large language models (AVLLMs) by introducing AVTrustBench, a comprehensive benchmark with 600K samples across 9 tasks, and proposed a training strategy (CAVPref) that achieved gains up to 30.19%.
With the rapid advancement of Multi-modal Large Language Models (MLLMs), several diagnostic benchmarks have recently been developed to assess these models' multi-modal 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 13 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, model-agnostic calibrated audio-visual preference optimization based training strategy CAVPref, obtaining a gain up to 30.19% across all 9 tasks. We will publicly release our code and benchmark to facilitate future research in this direction.