Chart parsing poses a significant challenge due to the diversity of styles,
values, texts, and so forth. Even advanced large vision-language models (LVLMs)
with billions of parameters struggle to handle such tasks satisfactorily. To
address this, we propose OneChart: a reliable agent specifically devised for
the structural extraction of chart information. Similar to popular LVLMs,
OneChart incorporates an autoregressive main body. Uniquely, to enhance the
reliability of the numerical parts of the output, we introduce an auxiliary
token placed at the beginning of the total tokens along with an additional
decoder. The numerically optimized (auxiliary) token allows subsequent tokens
for chart parsing to capture enhanced numerical features through causal
attention. Furthermore, with the aid of the auxiliary token, we have devised a
self-evaluation mechanism that enables the model to gauge the reliability of
its chart parsing results by providing confidence scores for the generated
content. Compared to current state-of-the-art (SOTA) chart parsing models,
e.g., DePlot, ChartVLM, ChartAst, OneChart significantly outperforms in Average
Precision (AP) for chart structural extraction across multiple public
benchmarks, despite enjoying only 0.2 billion parameters. Moreover, as a chart
parsing agent, it also brings 10%+ accuracy gains for the popular LVLM
(LLaVA-1.6) in the downstream ChartQA benchmark.

提出了一种可靠的代理 OneChart，用于图表信息的结构提取。通过引入辅助符号和额外的解码器，增强输出的数值部分的可靠性，并采用自我评估机制来衡量结果的可靠性，并在多个公共基准测试中显著优于当前一流的图表结构抽取模型。

OneChart：通过一个辅助标记提取纯净的图表结构

OneChart: Purify the Chart Structural Extraction via One Auxiliary Token

Structural extraction of events within discourse is critical since it avails
a deeper understanding of communication patterns and behavior trends. Event
argument extraction (EAE), at the core of event-centric understanding, is the
task of identifying role-specific text spans (i.e., arguments) for a given
event. Document-level EAE (DocEAE) focuses on arguments that are scattered
across an entire document. In this work, we explore the capabilities of open
source Large Language Models (LLMs), i.e., Flan-UL2, for the DocEAE task. To
this end, we propose ULTRA, a hierarchical framework that extracts event
arguments more cost-effectively -- the method needs as few as 50 annotations
and doesn't require hitting costly API endpoints. Further, it alleviates the
positional bias issue intrinsic to LLMs. ULTRA first sequentially reads text
chunks of a document to generate a candidate argument set, upon which ULTRA
learns to drop non-pertinent candidates through self-refinement. We further
introduce LEAFER to address the challenge LLMs face in locating the exact
boundary of an argument span. ULTRA outperforms strong baselines, which include
strong supervised models and ChatGPT, by 9.8% when evaluated by the exact match
(EM) metric.

本研究提出了一种基于开源大型语言模型（LLMs）的层次化框架 ULTRA，用于在文档级别进行事件参数抽取，该方法相较于强监督模型和 ChatGPT，在精确定位事件参数范围、减轻位置偏见等方面表现优越，并且在精确匹配（EM）指标上提高了 9.8％的性能。