paper-with-me

홈 › Papers

Evaluating Gemini in an arena for learning

2025-05-30 · LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Ankit Anand, Avishkar Bhoopchand, Brett Wiltshire, Daniel Gillick, Daniel Kasenberg, Eleni Sgouritsa, Gal Elidan, Hengrui Liu, Holger Winnemoeller, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Komal Singh, Lisa Wang, Markus Kunesch, Miruna Pîslar, Niv Efron, Parsa Mahmoudieh, Pierre-Alexandre Kamienny, Sara Wiltberger, Shakir Mohamed, Shashank Agarwal, Shubham Milind Phal, Sun Jae Lee, Theofilos Strinopoulos, Wei-Jen Ko, Yael Gold-Zamir, Yael Haramaty, Yannis Assael

Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-the-art support for educational use cases, we ran an "arena for learning" where educators and pedagogy experts conduct blind, head-to-head, multi-turn comparisons of leading AI models. In particular, $N = 189$ educators drew from their experience to role-play realistic learning use cases, interacting with two models sequentially, after which $N = 206$ experts judged which model better supported the user's learning goals. The arena evaluated a slate of state-of-the-art models: Gemini 2.5 Pro, Claude 3.7 Sonnet, GPT-4o, and OpenAI o3. Excluding ties, experts preferred Gemini 2.5 Pro in 73.2% of these match-ups -- ranking it first overall in the arena. Gemini 2.5 Pro also demonstrated markedly higher performance across key principles of good pedagogy. Altogether, these results position Gemini 2.5 Pro as a leading model for learning.

📄 PDF Abstract BibTeX arXiv:2505.24477

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Werewolf Arena: A Case Study in LLM Evaluation via Social Deduction

2024-07-18 · Suma Bailis, Jane Friedhoff, Feiyang Chen

This paper introduces Werewolf Arena, a novel framework for evaluating large language models (LLMs) through the lens of the classic social deduction game, Werewolf. In Werewolf Arena, LLMs compete against each other, nav…

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks

2025-06-02 · Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira, Atsuki Sato 외

Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become incre…

Large Language ModelMathematical Reasoning

360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents

2026-08-09 · Kenta Watanabe, Atsuyuki Miyai, Mizuki Takenawa, Kiyoharu Aizawa 외 hf

We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment constructed from 360-degree videos. Existing outdoor benchmarks either lack su…

Spatial Reasoning

OlympicArena Medal Ranks: Who Is the Most Intelligent AI So Far?

2024-06-24 · Zhen Huang, Zengzhi Wang, Shijie Xia, PengFei Liu

In this report, we pose the following question: Who is the most intelligent AI model to date, as measured by the OlympicArena (an Olympic-level, multi-discipline, multi-modal benchmark for superintelligent AI)? We specif…

ERPBench: Evaluating LLM Agents for Enterprise Decision-Making Across Competitive Market Ecologies

2026-09-04 · Xinran Zhang, Pengrui Lu, Lyumanshan Ye, Pengfei Liu arxiv

Large language model (LLM) agents are increasingly proposed for enterprise workflows, yet existing evaluations rarely test whether business-decision conclusions transfer across competitive market ecologies. We introduce …