ChartNet is a million-scale multimodal dataset for chart understanding created via code-guided synthesis spanning 24 chart types with five aligned modalities per sample.
Chartqapro: A more di- verse and challenging benchmark for chart question answer- ing
5 Pith papers cite this work. Polarity classification is still indexing.
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A new UML class diagram VQA benchmark and 16k dataset enable LoRA fine-tuning to outperform Qwen 3.5 27B.
Chart-RL uses RL policy optimization and LoRA to boost VLM chart reasoning, enabling a 4B model to reach 0.634 accuracy versus 0.580 for an 8B model with lower latency.
ChartVerse uses Rollout Posterior Entropy and truth-anchored inverse QA synthesis to produce 640K high-quality chart reasoning samples, training an 8B model that surpasses its 30B teacher.
CycleChart is a consistency-based framework that organizes chart generation, schema parsing, data parsing, and QA around single data instances to enforce bidirectional semantic alignment and improve cross-task generalization.
citing papers explorer
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ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
ChartNet is a million-scale multimodal dataset for chart understanding created via code-guided synthesis spanning 24 chart types with five aligned modalities per sample.
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Unlocking UML Class Diagram Understanding in Vision Language Models
A new UML class diagram VQA benchmark and 16k dataset enable LoRA fine-tuning to outperform Qwen 3.5 27B.
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Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
Chart-RL uses RL policy optimization and LoRA to boost VLM chart reasoning, enabling a 4B model to reach 0.634 accuracy versus 0.580 for an 8B model with lower latency.
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CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
CycleChart is a consistency-based framework that organizes chart generation, schema parsing, data parsing, and QA around single data instances to enforce bidirectional semantic alignment and improve cross-task generalization.