input_ids
listlengths 256
256
| attention_mask
listlengths 256
256
| sae_features
listlengths 24.6k
24.6k
|
|---|---|---|
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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
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]
- Repository: hallucination_circuits
- Paper: On the way!
- Demo: On the way!
Uses
Direct Use
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
Currently, we only have the train split from the ELI5 dataset. Therefore, this dataset is very similarly structured.
Dataset Creation
Curation Rationale
[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
The inputs are tokenized prompts. The labels are tokenized, embedded, and then encoded with the given SAE layer.
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Dataset Card Contact
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