English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the rapid development of large language models (LLMs) and theirintegration into large multimodal models (LMMs), there has been impressiveprogress in zero-shot completion of user-oriented vision-language tasks.However, a gap remains in the domain of chart image understanding due to thedistinct abstract components in charts. To address this, we introduce alarge-scale MultiModal Chart Instruction (MMC-Instruction) dataset comprising600k instances supporting diverse tasks and chart types. Leveraging this data,we develop MultiModal Chart Assistant (MMCA), an LMM that achievesstate-of-the-art performance on existing chart QA benchmarks. Recognizing theneed for a comprehensive evaluation of LMM chart understanding, we also proposea MultiModal Chart Benchmark (MMC-Benchmark), a comprehensive human-annotatedbenchmark with 9 distinct tasks evaluating reasoning capabilities over charts.Extensive experiments on MMC-Benchmark reveal the limitations of existing LMMson correctly interpreting charts, even for the most recent GPT-4V model. Ourwork provides an instruction-tuning methodology and benchmark to advancemultimodal understanding of charts.

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning