Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
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Computer Science > Computation and Language
arXiv:2606.16494 (cs)
[Submitted on 15 Jun 2026]
Title:Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
View a PDF of the paper titled Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering, by Jieyuan Liu and 4 other authors
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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 position dependence in multimodal KB-VQA: a gold-position protocol in which only the gold passage's prompt slot varies within question. We run it on three open-source 7B/8B VLM readers and two KB-VQA benchmarks at k up to 20. The shape flips from U to primacy: gold-at-first beats gold-at-last by 16 to 26 points on every reader-by-benchmark cell, an effect we call "Lost at the End". Three targeted ablations narrow the cause: a text-only control shows the multimodal setting amplifies an already-present text-mode primacy 2.2 to 4.5 times, and image-position and distractor-shuffle ablations together pin the locus to prompt slot 0 of the instruction-tuned reader. On a frozen reader, three retrieval-side fixes (MMR, oracle reranking, rank-based reordering) all leave the gap intact (no separable improvement). Our findings indicate that recall@k is the wrong metric for deployed KB-VQA and that closing the gap requires reader-side intervention; we release our protocol as a controlled instrument for evaluating such interventions.
| Comments: | 15 pages, 9 figures. Under review at EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| ACM classes: | I.2.7; I.2.10; H.3.3 |
| Cite as: | arXiv:2606.16494 [cs.CL] |
| (or arXiv:2606.16494v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.16494
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering, by Jieyuan Liu and 4 other authors
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