metadata
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configs:
- config_name: Qwen--Qwen2.5-Math-1.5B-Instruct
data_files:
- split: train
path: Qwen--Qwen2.5-Math-1.5B-Instruct/train-*
- split: validation
path: Qwen--Qwen2.5-Math-1.5B-Instruct/validation-*
- split: test
path: Qwen--Qwen2.5-Math-1.5B-Instruct/test-*
- config_name: Qwen--Qwen2.5-Math-7B-Instruct
data_files:
- split: train
path: Qwen--Qwen2.5-Math-7B-Instruct/train-*
- split: validation
path: Qwen--Qwen2.5-Math-7B-Instruct/validation-*
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path: Qwen--Qwen2.5-Math-7B-Instruct/test-*
- config_name: deepseek-ai--DeepSeek-R1-Distill-Qwen-7B
data_files:
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path: deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/train-*
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path: deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/validation-*
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path: deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/test-*
- config_name: openai--gpt-oss-20b_high
data_files:
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path: openai--gpt-oss-20b_high/train-*
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path: openai--gpt-oss-20b_high/validation-*
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path: openai--gpt-oss-20b_high/test-*
- config_name: openai--gpt-oss-20b_low
data_files:
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path: openai--gpt-oss-20b_low/train-*
- split: validation
path: openai--gpt-oss-20b_low/validation-*
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path: openai--gpt-oss-20b_low/test-*
- config_name: openai--gpt-oss-20b_medium
data_files:
- split: train
path: openai--gpt-oss-20b_medium/train-*
- split: validation
path: openai--gpt-oss-20b_medium/validation-*
- split: test
path: openai--gpt-oss-20b_medium/test-*
Generations Dataset: MATH
LLM-generated solutions across train/validation/test splits for multiple models.
Columns
| Column | Type | Description |
|---|---|---|
problem |
str | Problem statement |
generated_solutions |
list | Generated solutions with scores |
success_rate |
float | Fraction of correct generations |
majority_vote_is_correct |
int (0/1) | Whether majority vote is correct |
k |
int | Number of samples generated |
temperature |
float | Sampling temperature |
max_len |
int | Maximum generation length |
model_name |
str | Model used for generation |
Usage
from datasets import load_dataset
dataset = load_dataset("CoffeeGitta/difficulty-MATH-generations", name="<org--model>")
train = dataset["train"]
Citation
@article{lugoloobi_llms_2026,
title = {LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations},
url = {http://arxiv.org/abs/2602.09924},
author = {Lugoloobi, William and Foster, Thomas and Bankes, William and Russell, Chris},
year = {2026},
}