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

Current scientific fact-checking benchmarks exhibit several shortcomings,such as biases arising from crowd-sourced claims and an over-reliance ontext-based evidence. We present SCITAB, a challenging evaluation datasetconsisting of 1.2K expert-verified scientific claims that 1) originate fromauthentic scientific publications and 2) require compositional reasoning forverification. The claims are paired with evidence-containing scientific tablesannotated with labels. Through extensive evaluations, we demonstrate thatSCITAB poses a significant challenge to state-of-the-art models, includingtable-based pretraining models and large language models. All models exceptGPT-4 achieved performance barely above random guessing. Popular promptingtechniques, such as Chain-of-Thought, do not achieve much performance gains onSCITAB. Our analysis uncovers several unique challenges posed by SCITAB,including table grounding, claim ambiguity, and compositional reasoning. Ourcodes and data are publicly available at https://github.com/XinyuanLu00/SciTab.

SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables