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

When trying to answer complex questions, people often rely on multiplesources of information, such as visual, textual, and tabular data. Previousapproaches to this problem have focused on designing input features or modelstructure in the multi-modal space, which is inflexible for cross-modalreasoning or data-efficient training. In this paper, we call for an alternativeparadigm, which transforms the images and tables into unified languagerepresentations, so that we can simplify the task into a simpler textual QAproblem that can be solved using three steps: retrieval, ranking, andgeneration, all within a language space. This idea takes advantage of the powerof pre-trained language models and is implemented in a framework called Solar.Our experimental results show that Solar outperforms all existing methods by10.6-32.3 pts on two datasets, MultimodalQA and MMCoQA, across ten differentmetrics. Additionally, Solar achieves the best performance on the WebQAleaderboard

Unified Language Representation for Question Answering over Text, Tables, and Images