human-vs-ai
ai-vs-human-rubric-companion-data
Companion dataset for the AI-vs-human rubric study
This dataset is the data side of an anonymous NeurIPS submission. It pairs with a separate anonymous code repository that contains the runnable scripts, validators, and documentation. The two artifacts together reproduce every paper-facing headline number without re-running any API-backed stage. The code URL for review is https://anonymous.4open.science/r/codereviewer-47F3/README.md.
How to use
Download this… See the full description on the dataset page: https://huggingface.co/datasets/forreview43/ai-vs-human-rubric-companion-data.human-vs-ai-50kMore details coming soon.
human-vs-Ai-generated-datasetHumanVsAICode
Human vs. AI-Generated Code
Dataset Summary
This dataset is a large-scale collection of human-written and LLM-generated code designed to study differences in defect distribution, code quality, and security characteristics between human developers and modern AI code assistants.
It contains paired implementations of the same function across multiple authorship sources, spanning Python and Java, two widely adopted programming languages with distinct typing systems, paradigms… See the full description on the dataset page: https://huggingface.co/datasets/OSS-forge/HumanVsAICode.tulu_human_vs_aieli5-human-vs-ai
ELI5 Human vs AI (long-form)
This dataset is for training and evaluating AI-writing detectors. It was built
as a clean way to compare known AI text against known human text: every human
answer predates ChatGPT by more than three years, so it is genuinely human by
construction, and every AI answer was written by a named 2026 model, so its origin
is certain too. Most detection datasets have to guess at their labels; this one
does not.
ELI5 answers were chosen because they are… See the full description on the dataset page: https://huggingface.co/datasets/mild-rgb/eli5-human-vs-ai.