Encoder-Free Multimodal LLMs Learn Implicit Encoders

Key points

• Unlike previous multimodal LLMs, these models do not use modality-specific encoders to extract tokenized embeddings representing audio and image input; instead, they directly feed windows of samples, pixels, or spectral features to the Transformer language model.1
• We analyze whether the early layers of these models learn to function similarly to dedicated audio and visual encoders.1
• We find that these early layers do in fact act as "implicit encoders," encoding audio and visual inputs in a way that is largely invariant to their cross-modal context.1
• Furthermore, when performing multimodal reasoning tasks, we show that independently encoding the modalities throughout these early model layers achieves the same accuracy as jointly encoding them from the first layer onwards.1

Sources (1)

• [1]Encoder-Free Multimodal LLMs Learn Implicit Encoders
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:09 PM

  We empirically analyze two recently proposed encoder-free multimodal LLMs, Gemma-4 12B and Inkling-Small.

  Unlike previous multimodal LLMs, these models do not use modality-specific encoders to extract tokenized embeddings representing audio and image input; instead, they directly feed windows of samples, pixels, or spectral features to the Transformer language model.

Extractive summary: sentences quoted from the sources.