AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs
AVTrustBench assesses AVLLM reliability; CAVPref improves performance by 30.19%.
Key Findings
Methodology
AVTrustBench evaluates AVLLM capabilities with 600K samples across 9 tasks, focusing on adversarial attacks, compositional reasoning, and modality dependency. CAVPref is a model-agnostic optimization strategy that significantly enhances model robustness and reliability through calibrated audio-visual preference.
Key Results
- CAVPref achieved a 30.19% performance improvement across all tasks, significantly outperforming existing methods.
- Most existing models perform poorly in adversarial and compositional reasoning tasks, failing to achieve human-like comprehension.
- Experiments reveal significant differences in model dependency on audio versus video modalities.
Significance
This study introduces the first comprehensive benchmark for assessing AVLLMs, addressing the gap in audio understanding. It reveals current model limitations and provides a new approach for performance enhancement through CAVPref, guiding future research directions.
Technical Contribution
AVTrustBench is the first comprehensive benchmark for AVLLMs, while CAVPref introduces a new training method through audio-visual preference optimization, significantly improving model robustness.
Novelty
AVTrustBench is the first benchmark focused on AVLLM trustworthiness, and CAVPref introduces a novel approach to multimodal preference optimization by incorporating audio-visual calibration.
Limitations
- Existing models perform poorly on compositional reasoning tasks involving audio and video, especially in complex scenarios.
- CAVPref's improvement is limited in certain specific tasks.
Future Work
Future research could explore more complex multimodal tasks, develop more efficient audio-visual fusion strategies, and further optimize CAVPref to enhance its applicability across different tasks.
AI Executive Summary
The rapid development of multimodal large language models (MLLMs) has led to the need for assessing their multimodal reasoning capabilities. However, existing benchmarks focus primarily on visual aspects, neglecting holistic audio-visual understanding. AVTrustBench, with 600K samples across 9 tasks, provides the first comprehensive evaluation of AVLLMs, revealing significant shortcomings in adversarial attacks, compositional reasoning, and modality dependency.
To address these issues, the study introduces CAVPref, a model-agnostic audio-visual preference optimization strategy that significantly enhances model robustness and reliability. Experimental results show that CAVPref improves performance by 30.19% across all tasks, outperforming existing methods.
This research not only fills the gap in audio-visual understanding evaluation but also provides new directions for future research. By releasing the code and benchmark, the study encourages both academia and industry to explore more robust AVLLMs, advancing the field of multimodal AI.
Deep Analysis
Background
With the evolution of large language models (LLMs), multimodal large language models (MLLMs) have become a research focus. These models can process not only text but also images, videos, and audio. However, existing evaluation benchmarks focus on visual modalities, neglecting comprehensive audio-visual understanding.
Core Problem
Existing benchmarks fail to comprehensively evaluate AVLLM capabilities, especially in adversarial attacks, compositional reasoning, and modality dependency. There's a lack of evaluation of these models' reliability and robustness in complex audio-visual scenarios.
Innovation
AVTrustBench introduces the first comprehensive benchmark for AVLLMs, covering 9 tasks. CAVPref significantly improves model performance through audio-visual preference optimization, providing a new training method.
Methodology
- �� AVTrustBench includes 600K samples across 9 tasks to evaluate AVLLM capabilities.
- �� CAVPref enhances model robustness through audio-visual preference calibration.
- �� Semi-automatic annotation generates multiple-choice questions, reducing human costs.
Experiments
Experiments use the AVTrustBench benchmark to evaluate 13 state-of-the-art AVLLMs. By comparing different models' performance across 9 tasks, the study analyzes their shortcomings in adversarial attacks and compositional reasoning.
Results
CAVPref improves performance by 30.19% across all tasks, significantly outperforming existing methods. Most existing models perform poorly in adversarial and compositional reasoning tasks, failing to achieve human-like comprehension.
Applications
AVTrustBench and CAVPref can be used to develop more reliable AVLLMs, applicable in autonomous driving, intelligent surveillance, and other fields, enhancing the practical performance of multimodal AI.
Limitations & Outlook
Existing models perform poorly on compositional reasoning tasks involving audio and video, especially in complex scenarios. CAVPref's improvement is limited in certain specific tasks, requiring further optimization.
Plain Language Accessible to non-experts
Imagine a school where a teacher (model) needs to understand both students' spoken language and body language (audio-visual). Current teachers focus only on spoken language, ignoring body language. AVTrustBench is like a test evaluating teachers' ability to understand students' overall expression. CAVPref is a training program helping teachers better understand both spoken and body language, improving teaching effectiveness.
ELI14 Explained like you're 14
Imagine playing a game where you need to listen to music and watch a video at the same time. Current game characters focus only on the video, ignoring the music. AVTrustBench is like a test evaluating characters' ability to understand the overall game experience. CAVPref is an upgrade helping characters better understand both music and video, enhancing the gaming experience.
Glossary
Adversarial Attack
A method to deliberately disrupt model inputs to test its robustness.
Used in AVTrustBench to evaluate model performance under adverse conditions.
Compositional Reasoning
The ability to understand multi-event audio-visual inputs.
Evaluates model understanding in complex audio-visual scenarios.
Modality Dependency
The degree of model reliance on different input modalities.
Used in AVTrustBench to evaluate model dependency on audio-visual modalities.
CAVPref
An audio-visual preference optimization strategy to enhance model robustness.
Used to improve AVLLM performance on AVTrustBench.
AVTrustBench
A benchmark for evaluating AVLLM capabilities, with 600K samples and 9 tasks.
Used for comprehensive evaluation of AVLLMs.
Open Questions Unanswered questions from this research
- 1 How to enhance model compositional reasoning in complex audio-visual scenarios?
- 2 How to optimize CAVPref for broader task applicability?
Applications
Immediate Applications
Intelligent Surveillance
Enhancing anomaly detection capabilities of surveillance systems by improving AVLLM robustness.
Long-term Vision
Autonomous Driving
Improving safety and reliability of autonomous driving systems through better audio-visual understanding.
Abstract
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.