arXiv:2602.00462v4 Announce Type: replace-cross Abstract: Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM. Intriguingly, this mapping can be as simple as a shallow MLP transformation. To understand why LLMs can so readily process visual tokens, we need interpretability methods that reveal what is encoded in the visual token representations at every layer of LLM processing. In this work, we introduce LatentLens, a novel approach for mapping latent representations to
The increased adoption and complexity of multi-modal large language models necessitate improved interpretability tools to understand their internal workings.
Understanding how LLMs process visual information is crucial for developing more robust, reliable, and ethically aligned AI systems, especially for a sophisticated reader focused on AI safety and development.
This work introduces a new method to reveal the specific visual tokens LLMs are processing internally, offering enhanced insight into VLM functionality beyond previous black-box approaches.
- · AI developers
- · AI interpretability researchers
- · Multi-modal AI applications
- · Developers relying solely on black-box VLM understanding
Improved interpretability will accelerate the development and debugging of multi-modal AI models.
Greater transparency in visual processing could lead to more trustworthy and explainable AI in critical applications.
This could enable optimization of VLM architectures and training, potentially reducing computational costs and improving performance.
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Read at arXiv cs.AI