File size: 5,683 Bytes
13b4425 9cae6ce 13b4425 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
---
dataset_info:
features:
- name: question
dtype: string
description: The question text
- name: answer
dtype: string
description: The correct answer to the question
- name: task
dtype: string
description: The task type (Math-9, Math-11, Symbolic-Equal, Symbolic-Longer, Commonsense)
- name: noise_type
dtype: string
description: The noise type (Clean, Irrelevant, Inaccurate)
- name: difficulty
dtype: string
description: The difficulty level of noise (None, Easy, Medium, Hard)
- name: demos
dtype: string
description: Chain-of-thought demonstrations containing example questions and answers
- name: num_demo_thoughts
dtype: float
description: Average number of thinking steps in each demonstration
- name: num_demo_noisy_thoughts
dtype: float
description: Average number of noisy thinking steps in each demonstration
- name: noise_distribution
dtype: string
description: Type of noise distribution (fixed or random)
splits:
- name: test
num_bytes: 2001494847
num_examples: 184737
configs:
- config_name: default
data_files:
- split: test
path: test-*
---
# NoRa: Noisy Rationales Dataset
NoRa (Noisy Rationales) is a dataset specifically designed to evaluate the reasoning capabilities of Large Language Models (LLMs) when faced with noisy reasoning processes. This dataset contains reasoning tasks with both clean reasoning samples and samples with different types and difficulties of noise.
## Dataset Structure
The dataset is organized along three main attributes:
### 1. task (Task Types)
- **Math-9**: Mathematical operations in base-9
- **Math-11**: Mathematical operations in base-11
- **Symbolic-Equal**: Symbol manipulation with equal sequence lengths
- **Symbolic-Longer**: Symbol manipulation with longer sequence lengths
- **Commonsense**: Relational reasoning tasks
### 2. noise_type (Noise Types)
- **Clean**: Chain-of-thought demonstrations without noise
- **Irrelevant**: Noise that is irrelevant to the reasoning
- **Inaccurate**: Noise containing inaccurate information
### 3. difficulty (for noisy samples)
- **Easy**: Low-difficulty noise
- **Medium**: Medium-difficulty noise
- **Hard**: High-difficulty noise
- **None**: For Clean samples (no noise)
## Data Format
Each sample contains the following fields:
- `question`: The question text
- `answer`: The correct answer
- `task`: The task type (Math-9, Math-11, Symbolic-Equal, Symbolic-Longer, Commonsense)
- `noise_type`: The noise type (Clean, Irrelevant, Inaccurate)
- `difficulty`: The difficulty level (Easy, Medium, Hard, or None for Clean samples)
- `demos`: Chain-of-thought demonstrations containing example questions and answers
- `num_demo_thoughts`: Average number of thinking steps in each demonstration
- `num_demo_noisy_thoughts`: Average number of noisy thinking steps in each demonstration
- `noise_distribution`: Type of noise distribution, e.g., 'fixed' or 'random'
## Usage Examples
```python
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("TMLR-Group-HF/NoRa", cache_dir="./dataset_cache")
math11_examples = dataset["test"].filter(lambda example: example["task"] == "Math-11")
symbolic_equal_examples = dataset["test"].filter(lambda example: example["task"] == "Symbolic-Equal")
symbolic_longer_examples = dataset["test"].filter(lambda example: example["task"] == "Symbolic-Longer")
commonsense_examples = dataset["test"].filter(lambda example: example["task"] == "Commonsense")
# Filter by Split (noise type)
clean_examples = dataset["test"].filter(lambda example: example['noise_type'] == "Clean")
irrelevant_examples = dataset["test"].filter(lambda example: example['noise_type'] == "Irrelevant")
inaccurate_examples = dataset["test"].filter(lambda example: example['noise_type'] == "Inaccurate")
# Filter by Difficulty level
easy_examples = dataset["test"].filter(lambda example: example['difficulty'] == "Easy")
medium_examples = dataset["test"].filter(lambda example: example['difficulty'] == "Medium")
hard_examples = dataset["test"].filter(lambda example: example['difficulty'] == "Hard")
# Combined filtering
math9_irrelevant_hard = dataset["test"].filter(
lambda example: example["task"] == "Math-9"
and example['noise_type'] == "Irrelevant"
and example['difficulty'] == "Hard"
)
# Get a single sample
sample = dataset["test"][0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['answer']}")
print(f"Task: {sample['task']}")
print(f"Demos: ------------------------------")
# Function to create n-shot demos
def get_n_shot_demos(example, n=3):
demos = example['demos'].split('\n\n')[:n]
demos_text = '\n\n'.join(demos)
return demos_text
# Example of 3-shot prompt
demos = get_n_shot_demos(sample, n=3)
print(demos)
print(f"--------------------------------")
print(f"Noise Type: {sample['noise_type']}")
print(f"Difficulty: {sample['difficulty']}")
```
## Citation
If you use the NoRa dataset, please cite the original paper:
```
@inproceedings{zhou2024can,
title={Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?},
author={Zhou, Zhanke and Tao, Rong and Zhu, Jianing and Luo, Yiwen and Wang, Zengmao and Han, Bo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)},
year={2024},
url={https://openreview.net/pdf?id=FbuODM02ra}
}
```
## Contact
For questions or feedback regarding the dataset, please visit the [GitHub repository](https://github.com/tmlr-group/NoisyRationales).
|