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license: mit
language:
- en
tags:
- text
- conversations
- classification
pretty_name: Slop classifier dataset
---
# Slop classifier dataset
A human-annotated dataset for studying and classifying **AI-generated text that people perceive as “AI slop.”**
The dataset is built from samples collected from existing public datasets and annotated through the **Bench Labs SlopFinder** interface.
## Slop score
Each sample receives a score based on human votes:
```text
-1 = definitely slop
0 = undecided / neutral
+1 = not slop at all
```
The score represents **human judgment**, not an objective measure of quality, AI-generatedness, or factual correctness.
Multiple people may vote on the same sample. This allows agreement and disagreement between annotators to be preserved rather than forcing every sample into a single binary label.
## Data
Each annotation contains information such as:
* `content` — the text being evaluated
* `slop_score` — the submitted human rating
* `source_dataset` — the dataset the sample originated from
* `source_row_id` — the original sample identifier
* `content_hash` — identifier used to help detect duplicate content
* `created_at` — time the annotation was collected
Source samples may contain conversations rather than standalone text.
## Collection
Samples are selected from public datasets and presented randomly to contributors through SlopFinder.
Contributors are given a single continuous slider rather than a list of predefined categories. This is intended to make annotation quick and reduce the friction of contributing.
No account is required to submit a vote.
The dataset is continuously growing as more annotations are collected.
## Intended use
This dataset is intended for:
* training AI-slop classification models
* studying human perception of AI-generated writing
* evaluating whether text classifiers generalize across different sources
* research into stylistic characteristics associated with perceived AI slop
It should **not** be treated as a definitive benchmark for writing quality or AI-generatedness.
## Limitations
The labels represent subjective human judgments. Different contributors may have very different ideas of what constitutes “slop.”
The dataset may also contain biases introduced by:
* the source datasets used for sampling
* the population of contributors
* differences in individual definitions of “slop”
* repeated exposure to similar types of text
A score should therefore be interpreted as **perceived sloppiness**, rather than an objective property of the text.
## Contribute
This project is an early preview, and contributions are welcome.
Try SlopFinder and help expand the dataset:
[Bench Labs Slop Classifier](https://bench-labs.web.app/slopfinder.html)
## License
MIT
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