arXiv:2606.16494v1 Announce Type: new Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base. In pure-text long-context LLMs, retrieved-context use follows the U-shaped "lost-in-the-middle" effect of Liu et al. (2024): information at the start and end of context is used, the middle is lost. Whether this transfers to deployed multimodal KB-VQA is open. To close this gap, we design the first controlled probe of reader-side
The proliferation of advanced retrieval-augmented generation (RAG) and multimodal AI systems necessitates understanding their limitations for reliable real-world deployment.
Understanding 'primacy bias' in multimodal RAG is crucial for developing robust, accurate AI systems, particularly in critical applications where factual correctness is paramount.
This research provides direct insight into how multimodal AI systems process and prioritize information within long contexts, indicating a need for refined context integration strategies.
- · AI researchers
- · Developers of RAG systems
- · Users benefiting from more accurate AI
- · AI systems with unmitigated 'primacy bias'
- · Applications relying on naive context integration
System designers will need to implement more sophisticated context window management and attention mechanisms for multimodal RAG.
Improved RAG performance could accelerate the adoption of knowledge-based AI across industries.
More reliable AI systems reduce the cost of factual errors, potentially leading to trust being placed in AI for more sensitive tasks.
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Read at arXiv cs.CL