datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agent-reward-bench
AgentRewardBench
💾Code
📄Paper
🌐Website
🤗Dataset
💻Demo
🏆Leaderboard
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent TrajectoriesXing Han Lù, Amirhossein Kazemnejad*, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy*Core Contributor
Loading dataset
You can use the huggingface_hub library to load the dataset. The dataset is available on Huggingface Hub at… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/agent-reward-bench.CHASE-Code
CHASE: Challenging AI with Synthetic Evaluations
The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional human annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems. In this work, we introduce **CHASE**, a unified framework to synthetically generate challenging problems using LLMs without human involvement. For a given task… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/CHASE-Code.MCiteBench
MCiteBench Dataset
MCiteBench is a benchmark for evaluating the ability of Multimodal Large Language Models (MLLMs) to generate text with citations in multimodal contexts.
Websites: https://caiyuhu.github.io/MCiteBench
Paper: https://arxiv.org/abs/2503.02589
Code: https://github.com/caiyuhu/MCiteBench
Data Download
Please download the MCiteBench_full_dataset.zip. It contains the data.jsonl file and the visual_resources folder.
Data Statistics… See the full description on the dataset page: https://huggingface.co/datasets/caiyuhu/MCiteBench.CHASE-QA
CHASE: Challenging AI with Synthetic Evaluations
The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional human annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems. In this work, we introduce **CHASE**, a unified framework to synthetically generate challenging problems using LLMs without human involvement. For a given task… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/CHASE-QA.newspapers2024JourneyBench_MCOTmcqt-shopping-mcqa