Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering

• URL: http://arxiv.org/abs/2608.02907v1
• Date: Mon, 03 Aug 2026 21:46:08 GMT
• Title: Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering
• Abstract summary: We propose a probabilistic framework that models query-document importance as latent variables and adaptively infers posterior weights to downweight likely false negatives.<n>With closed-form posterior updates under conjugate priors and EM optimization, our method consistently improves retrieval accuracy across three retrievers and seven knowledge-based VQA benchmarks.
• Score: 30.082007822015544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. However, existing contrastive training methods typically treat all unmatched query-document pairs as equally informative negatives, which is problematic because many unmatched documents may still be semantically relevant or partially useful. We propose Bayesian Data Reweighting, a probabilistic framework that models query-document importance as latent variables and adaptively infers posterior weights to downweight likely false negatives. With closed-form posterior updates under conjugate priors and stochastic EM optimization, our method consistently improves retrieval accuracy across three retrievers and seven knowledge-based VQA benchmarks.

Related papers

• R$^3$AG: Retriever Routing for Retrieval-Augmented Generation [59.47703698994575]
  R$3$AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.<n>R$3$AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods.
  arXiv  Detail & Related papers  (2026-04-22T06:51:20Z)
• LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval [0.0]
  LITTA is a query-expansion-centric retrieval framework for evidence page retrieval.<n>Given a user query, LITTA generates complementary query variants using a large language model.<n>Candidates from expanded queries are aggregated through reciprocal rank fusion to improve evidence coverage.
  arXiv  Detail & Related papers  (2026-03-10T13:25:39Z)
• ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering [54.72902502486611]
  ReAG is a Reasoning-Augmented Multimodal RAG approach that combines coarse- and fine-grained retrieval with a critic model that filters irrelevant passages.<n>ReAG significantly outperforms prior methods, improving answer accuracy and providing interpretable reasoning grounded in retrieved evidence.
  arXiv  Detail & Related papers  (2025-11-27T19:01:02Z)
• Improving Document Retrieval Coherence for Semantically Equivalent Queries [63.97649988164166]
  We propose a variation of the Multi-Negative Ranking loss for training DR that improves the coherence of models in retrieving the same documents.<n>The loss penalizes discrepancies between the top-k ranked documents retrieved for diverse but semantic equivalent queries.
  arXiv  Detail & Related papers  (2025-08-11T13:34:59Z)
• Maximally-Informative Retrieval for State Space Model Generation [59.954191072042526]
  We introduce Retrieval In-Context Optimization (RICO) to minimize model uncertainty for a particular query at test-time.<n>Unlike traditional retrieval-augmented generation (RAG), which relies on externals for document retrieval, our approach leverages direct feedback from the model.<n>We show that standard top-$k$ retrieval with model gradients can approximate our optimization procedure, and provide connections to the leave-one-out loss.
  arXiv  Detail & Related papers  (2025-06-13T18:08:54Z)
• ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision [23.80886911344813]
  Multi-hop question answering involves reasoning across multiple documents to answer complex questions.<n>Dense retrievers typically outperform sparse methods like BM25 by leveraging semantic embeddings.<n>ReSCORE is a novel method for training dense retrievers for MHQA without labeled documents.
  arXiv  Detail & Related papers  (2025-05-27T14:28:24Z)
• Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines [17.803396998387665]
  Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task.<n>We propose ReAuSE, an alternative to the previous RAG model for the knowledge-based VQA task.<n>Our model functions both as a generative retriever and an accurate answer generator.
  arXiv  Detail & Related papers  (2025-02-23T16:39:39Z)
• Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training [81.3781338418574]
  We propose relevance-aware contrastive learning. We consistently improve the SOTA unsupervised Contriever model on the BEIR and open-domain QA retrieval benchmarks. Our method can not only beat BM25 after further pre-training on the target corpus but also serves as a good few-shot learner.
  arXiv  Detail & Related papers  (2023-06-05T18:20:27Z)
• Questions Are All You Need to Train a Dense Passage Retriever [123.13872383489172]
  ART is a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. It uses a new document-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence documents, and (2) the documents are then used to compute the probability of reconstructing the original question.
  arXiv  Detail & Related papers  (2022-06-21T18:16:31Z)
• End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering [36.80395759543162]
  We present an end-to-end differentiable training method for retrieval-augmented open-domain question answering systems. We model retrieval decisions as latent variables over sets of relevant documents. Our proposed method outperforms all existing approaches of comparable size by 2-3% exact match points.
  arXiv  Detail & Related papers  (2021-06-09T19:25:37Z)
• Mitigating False-Negative Contexts in Multi-document QuestionAnswering with Retrieval Marginalization [29.797379277423143]
  We develop a new parameterization of set-valued retrieval that properly handles unanswerable queries. We show that marginalizing over this set during training allows a model to mitigate false negatives in annotated supporting evidences. On IIRC, we show that joint modeling with marginalization on alternative contexts improves model performance by 5.5 F1 points and achieves a new state-of-the-art performance of 50.6 F1.
  arXiv  Detail & Related papers  (2021-03-22T23:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.