Omkar2118/datasets huggingface dataset:
Arena-Hard

The **Arena-Hard benchmark** is a high-quality benchmarking tool for Language Learning Models (LLMs) developed by LMSYS Org¹. It was designed to address the limitations of traditional benchmarks, which are often static or close-ended¹.

Key features of the Arena-Hard benchmark include¹²:
- **Robustly separates model capability**: It can differentiate the capabilities of various models.
- **Reflects human preference in real-world use cases**: The benchmark score has a high agreement with human preference.
- **Frequently updates to avoid over-fitting or test set leakage**: It uses new, unseen prompts to ensure the benchmark remains challenging and relevant.

The Arena-Hard benchmark is built from live data in the Chatbot Arena, a crowd-sourced platform for LLM evaluations¹. It contains 500 challenging user queries². The benchmark uses GPT-4-Turbo as a judge to compare the responses of different models against a baseline model².

The Arena-Hard benchmark has been found to offer significantly stronger separability against other benchmarks, with tighter confidence intervals¹. It also has a higher agreement (89.1%) with the human preference ranking by Chatbot Arena¹.

(1) From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline .... https://lmsys.org/blog/2024-04-19-arena-hard/.
(2) GitHub - lm-sys/arena-hard: Arena-Hard benchmark. https://github.com/lm-sys/arena-hard.
(3) From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline .... https://lmsys.org/blog/2024-04-19-arena-hard/.
(4) GitHub - lm-sys/arena-hard: Arena-Hard benchmark. https://github.com/lm-sys/arena-hard.
(5) undefined. https://github.com/lm-sys/arena-hard.git.
(6) undefined. https://huggingface.co/spaces/lmsys/arena-hard-browser.
LOGO

LOGO is a multi-person long-form video dataset with frame-wise annotations on both action procedures and formations based on artistic swimming scenarios. It provides a potential for constructing an action quality assessment approach with the ability to model group information among actors. Longer video durations also challenge the ability of the method to aggregate long-term temporal information.

Introducing paper: LOGO: A Long-Form Video Dataset for Group Action Quality Assessment
Integrable Expressions

A curated dataset using the methodology of the paper is available in the `Dataset` folder. The name of the JSON for each file corresponds to the elementary extension used to generate the (integrand, integral) pair. Each JSON contains 10,000 examples. These can be directly read as Maple expressions using the `parse` command in Maple or to Python SymPy Expressions using the `sympify` command.

Introducing paper: Generating Elementary Integrable Expressions
Deep Learning for Symbolic Mathematics

Integrals and Differential Equations Dataset using the generators from the paper

Introducing paper: Deep Learning for Symbolic Mathematics