Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
TL;DR
Multimodal Large Language Models (MLLMs) lack clarity on how audio and visual signals influence predictions. This study investigates the information flow in Audio-Visual Large Language Models (AVLLMs) using two input configurations.
✦ Why It Matters
Engineers can leverage these findings to design more efficient AVLLMs that maintain performance while reducing computational load.
Key Takeaways
- Explore AVLLMs for integrating audio and visual data.
- Consider parallel processing for interleaved audio-visual items.
- Implement token discarding strategies to enhance efficiency.
- Utilize findings to improve model interpretability and design.
Full Summary
Multimodal Large Language Models (MLLMs) are increasingly used in AI but understanding how they process audio and visual information remains limited. This research focuses on Audio-Visual Large Language Models (AVLLMs) and examines their internal pathways for integrating audio and visual signals.
Two configurations were tested: audio-visual video and multiple interleaved audio-visual items. Results indicate that AVLLMs follow a sequential information flow for video inputs, while interleaved items shift to parallel processing streams.
Notably, the study found that certain audio-visual tokens could be discarded after their information was utilized, leading to more efficient inference with minimal impact on predictions. These insights were consistent across different models, including Qwen2.5-Omni and Video-SALMONN2 Plus.
This research lays the groundwork for future advancements in the interpretability and efficiency of MLLMs.