# Dataset Card for gpt2_eli5_sae_features

This dataset aims to create a corpus of data to help guide research into monosemantic features using SAE's. It has been generated using [this raw template](https://github.com/manik-sethi/hallucination-circuits)

## Dataset Details

### Dataset Description
This dataset takes the eli5 subset from facebook/kilt_tasks and processes it to be used in SAE research. More specifically, we take the inputs, and tokenize them using the gpt2-small tokenizer. The outputs are tokenized, embedded , and encoded using the 



- **Curated by:** Manik Sethi
- **License:** MIT

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** [hallucination_circuits](https://github.com/manik-sethi/hallucination-circuits)
- **Paper:** On the way!
- **Demo:** On the way!

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset is meant to train a multi-layer-perceptron to predict what the SAE feature activations would be in the answer for a prompt. Note that this dataset doesn't actually have the answers to the tokenized questions, but rather a ~25k long vector of activations for different features in a trained SAE.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Currently, we only have the train split from the ELI5 dataset. Therefore, this dataset is very similarly structured. 

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

The source of this data is from the subreddit "Explain Like I'm 5", or ELI5 for short.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The inputs are tokenized prompts. The labels are tokenized, embedded, and then encoded with the given SAE layer.



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.


## Dataset Card Contact

mksethi@ucdavis.edu