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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 the sequential and parallel processing pathways in AVLLMs.
- Test the impact of discarding audio-visual tokens on model predictions.
- Utilize findings to improve model interpretability and design.
- Apply insights across different AVLLM architectures for enhanced performance.
How It Works
AVLLMs process audio and visual inputs through distinct pathways. For video inputs, they follow a sequential flow, integrating audio and visual data based on task demands.
In contrast, when handling interleaved items, they switch to parallel processing, allowing for more efficient information handling. This flexibility in routing enables the model to optimize performance based on the input type.