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---
license: unknown
task_categories:
- text-classification
language:
- en
pretty_name: sst2_cognitive-bias
size_categories:
- 100K<n<1M
source_datasets:
- sst2
dataset_info:
  features:
  - name: idx
    dtype: string
  - name: sentence
    dtype: string
  - name: label
    dtype: int64
  - name: dist
    dtype: int64
  - name: shot1_idx
    dtype: string
  - name: shot1_sent
    dtype: string
  - name: shot1_label
    dtype: int64
  - name: shot2_idx
    dtype: string
  - name: shot2_sent
    dtype: string
  - name: shot2_label
    dtype: int64
  - name: shot3_idx
    dtype: string
  - name: shot3_sent
    dtype: string
  - name: shot3_label
    dtype: int64
  - name: shot4_idx
    dtype: string
  - name: shot4_sent
    dtype: string
  - name: shot4_label
    dtype: int64
  - name: few_shot_string
    dtype: string
  - name: few_shot_hard_string
    dtype: string
  - name: id
    dtype: int64
  splits:
  - name: train
    num_bytes: 286790625
    num_examples: 250000
  download_size: 47727501
  dataset_size: 286790625
splits:
- name: train
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card for sst2_cognitive-bias

This dataset is a modification of the original [SST-2](https://huggingface.co/datasets/stanfordnlp/sst2) dataset for LLM cognitive bias evaluation.

## Language(s)

- English (`en`)

## Dataset Summary

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. 
The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.

## Dataset Structure

The modifications carried out in the dataset are thought to evaluate cognitive biases in a few-shot setting and with different task complexities. 
We make use of 25k instances from the original dataset, while the remaining ones serve as few-shot examples.
Each instance is prompted with all possible unbalanced 4-shot distributions.
We increase task complexity by introducing an additional neutral example between the first and last two examples.

## Supported Tasks and Leaderboards

- `sentiment-classification`

## Citation

[In Press]

**BibTeX:**

[In Press]