← 研踪 PaperTrailarXiv:2611.03023 · v1
VizDAGuide: Misleading Chart Question Answering in Compact Vision Language Models via DAG-Structured Supervision
首次提交 2026-11-02 20:20
版本更新 2026-11-02 20:20
雷达观测 2026-11-04 23:42
摘要
Misleading charts can lead vision-language models to conclusions that conflict with underlying data. Existing work focuses on inference-time strategies, while training-time supervision remains less explored. We present VizDAGuide, a DAG-structured supervision method for misleading-chart question answering. The method represents intermediate reasoning states and their dependencies as a directed acyclic graph, assigns supervision weights according to their roles, and serializes the graph into a unified training target for open-ended and multiple-choice QA with single-pass inference. Experiments on two benchmarks across Qwen3-VL and InternVL3.5 models show improvements over a content- and weight-matched Linear CoT-SFT baseline on both answer formats. Process-level evaluation shows gains in grounding and operation execution, while ablations indicate that dependency structure and weighted supervision are most effective when combined. These results support DAG-structured weighted supervision for misleading-chart question answering.
作者备注：21 pages, 16 figures
观测到的版本
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v1 · 2026-11-02 20:20 · 最新观测版本
VizDAGuide: Misleading Chart Question Answering in Compact Vision Language Models via DAG-Structured Supervision
Misleading charts can lead vision-language models to conclusions that conflict with underlying data. Existing work focuses on inference-time strategies, while training-time supervision remains less explored. We present VizDAGuide, a DAG-structured supervision method for misleading-chart question answering. The method represents intermediate reasoning states and their dependencies as a directed acyclic graph, assigns supervision weights according to their roles, and serializes the graph into a unified training target for open-ended and multiple-choice QA with single-pass inference. Experiments on two benchmarks across Qwen3-VL and InternVL3.5 models show improvements over a content- and weight-matched Linear CoT-SFT baseline on both answer formats. Process-level evaluation shows gains in grounding and operation execution, while ablations indicate that dependency structure and weighted supervision are most effective when combined. These results support DAG-structured weighted supervision for misleading-chart question answering.
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