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

This paper presents GRASP, a novel benchmark to evaluate the languagegrounding and physical understanding capabilities of video-based multimodallarge language models (LLMs). This evaluation is accomplished via a two-tierapproach leveraging Unity simulations. The initial level tests for languagegrounding by assessing a model's ability to relate simple textual descriptionswith visual information. The second level evaluates the model's understandingof 'Intuitive Physics' principles, such as object permanence and continuity. Inaddition to releasing the benchmark, we use it to evaluate severalstate-of-the-art multimodal LLMs. Our evaluation reveals significantshortcomings in current models' language grounding and intuitive physics. Theseidentified limitations underline the importance of benchmarks like GRASP tomonitor the progress of future models in developing these competencies.

GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models