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dataset_info.json
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{
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"dataset_info": {
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"description": "NoRa (Noisy Rationales) is a dataset designed to evaluate the reasoning capabilities of Large Language Models (LLMs) when faced with noisy reasoning processes.",
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"citation": "@inproceedings{zhou2024can,\n title={Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?},\n author={Zhou, Zhanke and Tao, Rong and Zhu, Jianing and Luo, Yiwen and Wang, Zengmao and Han, Bo},\n booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)},\n year={2024},\n url={https://openreview.net/pdf?id=FbuODM02ra}\n}",
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"homepage": "https://github.com/tmlr-group/NoisyRationales",
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"license": "cc-by-4.0",
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"features": [
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{
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"name": "question",
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"dtype": "string",
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"description": "The question text"
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},
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{
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"name": "answer",
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"dtype": "string",
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"description": "The correct answer to the question"
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},
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{
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"name": "task",
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"dtype": "string",
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"description": "The task type (Math-9, Math-11, Symbolic-Equal, Symbolic-Longer, Commonsense)"
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},
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{
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"name": "noise_type",
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"dtype": "string",
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"description": "The type of noise (Clean, Irrelevant, Inaccurate)"
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},
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{
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"name": "difficulty",
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"dtype": "string",
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"description": "The difficulty level of noise (None, Easy, Medium, Hard)"
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},
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{
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"name": "demos",
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"dtype": "string",
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"description": "Chain-of-thought demonstrations containing example questions and answers"
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},
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{
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"name": "num_demo_thoughts",
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"dtype": "float",
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"description": "Average number of thinking steps in each demonstration"
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},
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{
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"name": "num_demo_noisy_thoughts",
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"dtype": "float",
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"description": "Average number of noisy thinking steps in each demonstration"
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},
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{
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"name": "noise_distribution",
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"dtype": "string",
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"description": "Type of noise distribution (fixed or random)"
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}
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],
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"splits": [
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{
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"name": "test",
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"num_bytes": 2001494847,
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"num_examples": 184737
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}
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]
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},
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"configs": [
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{
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"config_name": "default",
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"data_files": [
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{
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"split": "test",
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"path": "test-*"
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}
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]
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}
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]
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}
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