SciArena

SciArena is an experimental platform designed for evaluating and comparing foundation language models based on their ability to generate literature reviews from scientific article databases. It utilizes a large-scale corpus from Semantic Scholar, which hosts over 200 million scientific articles across multiple disciplines. The platform allows anonymous side-by-side comparison of model-generated answers to research questions, with user votes contributing to an ongoing leaderboard ranking.

The tool leverages an information retrieval mechanism adapted from Scholar QA, feeding retrieved data to randomly selected models that produce literature review-style answers. This setup enables assessment of different models’ performance in synthesizing and summarizing scientific literature. While SciArena provides insights into model capabilities using well-established scholarly data, it is limited by a delay in indexing very recent publications, updating roughly annually.

Key features include anonymous model comparison for unbiased evaluation, integration with Semantic Scholar’s comprehensive scientific repository, and a voting system that crowdsources quality judgments. SciArena supports research and AI development by providing a transparent benchmarking environment for language models engaged in academic content generation and review tasks. It operates as a free, open platform facilitating ongoing foundational model assessment in scientific domains.

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Website: sciarena.allen.ai

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