SciArena: An Open Evaluation Platform for Foundation Models in
  Scientific Literature Tasks
Authors: name: Yilun Zhao, name: Kaiyan Zhang, name: Tiansheng Hu, name: Sihong Wu, name: Ronan Le Bras, name: Taira Anderson, name: Jonathan Bragg, name: Joseph Chee Chang, name: Jesse Dodge, name: Matt Latzke, name: Yixin Liu, name: Charles McGrady, name: Xiangru Tang, name: Zihang Wang, name: Chen Zhao, name: Hannaneh Hajishirzi, name: Doug Downey, name: Arman Cohan

We present SciArena, an open and collaborative platform for evaluating
foundation models on scientific literature tasks. Unlike traditional benchmarks
for scientific literature understanding and synthesis, SciArena engages the
research community directly, following the Chatbot Arena evaluation approach of
community voting on model comparisons. By leveraging collective intelligence,
SciArena offers a community-driven evaluation of model performance on
open-ended scientific tasks that demand literature-grounded, long-form
responses. The platform currently supports 23 open-source and proprietary
foundation models and has collected over 13,000 votes from trusted researchers
across diverse scientific domains. We analyze the data collected so far and
confirm that the submitted questions are diverse, aligned with real-world
literature needs, and that participating researchers demonstrate strong
self-consistency and inter-annotator agreement in their evaluations. We discuss
the results and insights based on the model ranking leaderboard. To further
promote research in building model-based automated evaluation systems for
literature tasks, we release SciArena-Eval, a meta-evaluation benchmark based
on our collected preference data. The benchmark measures the accuracy of models
in judging answer quality by comparing their pairwise assessments with human
votes. Our experiments highlight the benchmark's challenges and emphasize the
need for more reliable automated evaluation methods.