Causal Attention Revisited to Fix Vision-Language Misalignment in MLLMs
A new paper on arXiv (2503.02597) identifies vision-language misalignment in multimodal large language models (MLLMs) as a critical challenge, where textual responses fail to factually align with text-image inputs. The authors argue that the root cause lies in the causal attention mechanism used by decoder-only LLMs, which limits earlier modalities (e.g., images) from incorporating information from later modalities (e.g., text). They propose revisiting the core architecture to unlock modality-mutual attention, offering a fundamental perspective beyond existing solutions like specialized connectors or visual instruction tuning.
Key facts
- arXiv paper 2503.02597 addresses vision-language misalignment in MLLMs.
- The paper argues causal attention in decoder-only LLMs limits cross-modal information flow.
- Existing solutions include specialized vision-language connectors and visual instruction tuning.
- The proposed approach revisits the core architecture for modality-mutual attention.
- The paper is categorized as a cross-post (replace-cross) on arXiv.
- MLLMs have shown progress in perceiving and reasoning over multimodal inquiries.
- Vision-language misalignment causes textual responses not factually aligned with inputs.
- The paper offers a fundamental perspective on the misalignment problem.
Entities
Institutions
- arXiv