SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks

Yixin Liu (Yale University) · Robert Tang (Yale University) · Yilun Zhao (Yale University) · Kaiyan Zhang (Wayfair) · Tiansheng Hu (NYU Shanghai) · Sihong Wu (Fudan University) · Ronan Le Bras (Allen Institute for AI) · Joseph Chee Chang (Allen Institute for Artificial Intelligence) · Jesse Dodge (Meta (FAIR)) · Jonathan Bragg (Allen Institute for Artificial Intelligence) · Chen Zhao (Nanjing University) · Hanna Hajishirzi (University of Washington/AI2) · Doug Downey (Allen Institute for Artificial Intelligence) · Arman Cohan (Yale University)
answer qualityautomated evaluation systemscollective intelligencecommunity-driven evaluationfoundation modelslong-form responsesmeta-evaluation benchmarkmodel performancemodel ranking leaderboardopen-ended taskspairwise assessmentspreference dataresearch communitysciarenascientific literature

We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature-grounded 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 44 open-source and proprietary foundation models and has collected over 19,000 votes from human researchers across diverse scientific domains. Our analysis of the data collected so far confirms its high quality.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.