Datasets and benchmarks

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6 entries. All categories. JSON: /api/library/datasets-benchmarks.json.

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Datasets and benchmarks

τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

2024-06-17 · arXiv · research-paper · Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan

Benchmark of dynamic conversations between a language-model-simulated user and an agent equipped with domain-specific API tools and policy guidelines. Success is scored by comparing the final database state with an annotated goal state, and the paper introduces the pass^k reliability metric.

GAIA: a benchmark for General AI Assistants

2023-11-21 · arXiv · research-paper · Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom

Benchmark of real-world questions for general AI assistants that require reasoning, multimodality, web browsing and tool use.

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

2023-10-10 · arXiv · research-paper · Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan

Evaluation framework of 2,294 software engineering problems drawn from real GitHub issues and pull requests across 12 popular Python repositories.

WebArena: A Realistic Web Environment for Building Autonomous Agents

2023-07-25 · arXiv · research-paper · Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig

Highly realistic and reproducible web environment for building and testing language-guided agents that perform tasks on the web. The authors list shows the first 12 of a longer list.

SWE-bench Leaderboards

Undated · SWE-bench · docs

Official SWE-bench site with leaderboards for SWE-bench, Verified, Multilingual, Multimodal and Lite variants, plus related tools.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Undated · Open X-Embodiment Collaboration · docs

Project page of the Open X-Embodiment Collaboration presenting robotic learning datasets and RT-X models. It asks whether the consolidation around large pretrained models seen in NLP and computer vision can happen in robotics.