Papers Chart Question Answering
“Chart Question Answering” 태그가 달린 논문 65편 · 필터 해제
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning…
Chart Question AnsweringMultimodal ReasoningLogical ReasoningVisual ReasoningEnhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: standard cross-entropy treats numeric toke…
Chart Question AnsweringMathematical ReasoningAgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA
Financial chart question answering in regulated settings demands more than accuracy: practitioners must know which answers to trust before acting on them, and many institutions cannot send client data to external model p…
Chart Question AnsweringSAFE-Cascade: Cost-Adaptive Vision-Language Routing for Chart Question Answering
Vision-language models (VLMs) are powerful for chart question answering, but invoking a VLM for every query can be unnecessarily expensive when many questions are answerable from OCR text and lightweight language reasoni…
Chart Question AnsweringVisual GroundingHierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning
Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at understanding single plots, they often stru…
Chart Question AnsweringChart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with v…
parameter-efficient fine-tuningChart Question AnsweringReinforcement LearningVisual ReasoningNavigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations. We present ChartCynics, an agentic dual-…
Chart Question AnsweringChart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards
Accurate chart comprehension represents a critical challenge in advancing multimodal learning systems, as extensive information is compressed into structured visual representations. However, existing vision-language mode…
Chart Question AnsweringReinforcement LearningDomain GeneralizationEvaluating Prompting Strategies for Chart Question Answering with Large Language Models
Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored. We present a systematic evaluation of four widely used prompting paradigms (Zero-Shot, Few-Shot, Zero-Shot Ch…
Chart Question AnsweringCycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
Current chart-related tasks, such as chart generation (NL2Chart), chart schema parsing, chart data parsing, and chart question answering (ChartQA), are typically studied in isolation, preventing models from learning the …
Chart Question AnsweringChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textua…
Visual Question AnsweringChart Question AnsweringVisual ReasoningVision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods rely heavily on manually curated datasets…
Chart Question AnsweringReinforcement LearningChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement
Charts are a crucial visual medium for communicating and representing information. While Large Vision-Language Models (LVLMs) have made progress on chart question answering (CQA), the task remains challenging, particular…
Chart Question AnsweringPOLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering
Charts are a universally adopted medium for data communication, yet existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. To address t…
Chart Question AnsweringChart Question Answering from Real-World Analytical Narratives
We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narra…
Chart Question AnsweringChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering
Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reasoning tasks remains an open challenge. Exi…
Chart Question AnsweringImage to textMultimodal ReasoningQuestion Answering+1ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual feat…
Chart Question AnsweringChart UnderstandingInstruction FollowingOptical Character Recognition (OCR)+1ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
The emergence of Multi-modal Large Language Models (MLLMs) presents new opportunities for chart understanding. However, due to the fine-grained nature of these tasks, applying MLLMs typically requires large, high-quality…
Chart Question AnsweringChart UnderstandingQuestion AnsweringRetrievalChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Chart understanding presents a unique challenge for large vision-language models (LVLMs), as it requires the integration of sophisticated textual and visual reasoning capabilities. However, current LVLMs exhibit a notabl…
Chart Question AnsweringChart UnderstandingQuestion AnsweringVisual ReasoningJudging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
Charts are ubiquitous as they help people understand and reason with data. Recently, various downstream tasks, such as chart question answering, chart2text, and fact-checking, have emerged. Large Vision-Language Models (…
Chart Question AnsweringFact CheckingInformativenessInstruction Following+1