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# 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
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### Direct Use
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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
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[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
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[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 |