  Hypothesis formulation and testing are central to empirical research. A
strong hypothesis is a best guess based on existing evidence and informed by a
comprehensive view of relevant literature. However, with exponential increase
in the number of scientific articles published annually, manual aggregation and
synthesis of evidence related to a given hypothesis is a challenge. Our work
explores the ability of current large language models (LLMs) to discern
evidence in support or refute of specific hypotheses based on the text of
scientific abstracts. We share a novel dataset for the task of scientific
hypothesis evidencing using community-driven annotations of studies in the
social sciences. We compare the performance of LLMs to several state-of-the-art
benchmarks and highlight opportunities for future research in this area. The
dataset is available at
https://github.com/Sai90000/ScientificHypothesisEvidencing.git
