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ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding

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arxiv 2409.03277 v3 pith:QOBZJVED submitted 2024-09-05 cs.AI cs.CLcs.CV

Zhengzhuo Xu , Bowen Qu , Yiyan Qi , Sinan Du , Chengjin Xu , Chun Yuan , Jian Guo This is my paper
classification cs.AIcs.CLcs.CV
keywords connectoralignmentchartchartmoeunderstandingdifferentdiverselyexpert
verification ladder T0 review T1 audit T2 compute T3 formal
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Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. However, current MLLMs still struggle to provide faithful data and reliable analysis only based on charts. To address it, we propose ChartMoE, which employs the Mixture of Expert (MoE) architecture to replace the traditional linear projector to bridge the modality gap. Specifically, we train several linear connectors through distinct alignment tasks, which are utilized as the foundational initialization parameters for different experts. Additionally, we introduce ChartMoE-Align, a dataset with nearly 1 million chart-table-JSON-code quadruples to conduct three alignment tasks (chart-table/JSON/code). Combined with the vanilla connector, we initialize different experts diversely and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters. Extensive experiments demonstrate the effectiveness of the MoE connector and our initialization strategy, e.g., ChartMoE improves the accuracy of the previous state-of-the-art from 80.48\% to 84.64\% on the ChartQA benchmark.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning

    cs.CL 2026-08 reject novelty 6.0 of 10

    LongChart is a graph-consistent multi-chart VQA benchmark where 10 multimodal LLMs lose accuracy as question reasoning hops grow.

  2. Visual Programmability: A Guide for Code-as-Thought in Chart Understanding

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A vision-language model learns to dynamically switch between code-based and visual reasoning for chart questions, improving average accuracy by about one point over fixed strategies.

  3. TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding

    cs.AI 2025-06 conditional novelty 6.0 of 10

    TableMoE is a multimodal table model whose role-aware router sends table tokens to HTML, JSON, and code experts and reports state-of-the-art results on its own WildStruct benchmarks and MMMU-Table.

  4. Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience

    cs.CL 2025-06 conditional novelty 6.0 of 10

    CoRE improves structured knowledge reasoning by retrieving both correct and incorrect past examples into the prompt, using MCTS-generated experience memory.

  5. VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection

    cs.CV 2025-05 conditional novelty 6.0 of 10

    VisTA uses GRPO reinforcement learning to train a vision-language agent to select external visual tools for a frozen reasoning model, improving accuracy on ChartQA, Geometry3K, BlindTest, and MathVerse.

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