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01mlfoundations-dev /math_stratos_scale_judged_and_annotated_with_difficultytabular100K<n<1M0 likes242 downloads2y agoHugging Face02agentlans /prompt-difficulty Prompt Difficulty Assessment Prompt difficulty plays a critical role in the performance of large language models (LLMs). Assessing this difficulty is essential for selecting training examples, evaluating model capabilities, and optimizing routing and reasoning strategies. Yet, no standardized framework exists for comparing prompt difficulty across domains. This report proposes a method to quantify prompt difficulty using multiple LLMs and introduces a composite difficulty score for… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty.tabulartext-classification10K<n<100K0 likes186 downloads9mo agoHugging Face03lime-nlp /DeepScaleR_Difficulty Difficulty Estimation on DeepScaleR We annotate the entire DeepScaleR dataset with a difficulty score based on the performance of the Qwen 2.5-MATH-7B model. This provides an adaptive signal for curriculum construction and model evaluation. DeepScaleR is a curated dataset of 40,000 reasoning-intensive problems used to train and evaluate reinforcement learning-based methods for large language models. Difficulty Scoring Method Difficulty scores are estimated using the… See the full description on the dataset page: https://huggingface.co/datasets/lime-nlp/DeepScaleR_Difficulty.tabularreinforcement-learning1M<n<10M11 likes128 downloads1y agoHugging Face04vibhuiitj /Exercise-Synthetic-split-ncert-chapter-mapped_filtered_difficulty_scoredtabular1M<n<10M0 likes128 downloads5mo agoHugging Face05lime-nlp /GSM8K_Difficulty Difficulty Estimation on DeepScaleR We annotate the entire GSM8K dataset with a difficulty score based on the performance of the Qwen 2.5-MATH-7B model. This provides an adaptive signal for curriculum construction and model evaluation. GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/lime-nlp/GSM8K_Difficulty.tabular1M<n<10M1 likes110 downloads1y agoHugging Face06ronantakizawa /aozora-text-difficulty Aozora Text Difficulty Dataset This dataset contains Japanese literary texts from the Aozora Bunko digital library, enhanced with jReadability-based difficulty analysis for Japanese language learning and curriculum development. Dataset Overview Source: Aozora Bunko (青空文庫) - Japan's premier digital library of public domain literature Enhancement: jReadability-based difficulty scoring using research-backed Japanese readability models Primary Methodology: jReadability - A… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/aozora-text-difficulty.tabulartext-classification1K<n<10K1 likes101 downloads10mo agoHugging Face07taesiri /BlindLoop-Difficulty-Feedbackgated BlindLoop Difficulty Feedback This is the public, hash-bound release of BlindLoop Section 3. Coding agents generated executable visual-question tasks; each task's inverse program checked the answer from rendered pixels. For complete feedback transactions, the exact same five images were evaluated by three frontier VLMs and the resulting difficulty signal was returned to the next generation episode. Contents Config Unit Rows tasks generated task 266… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/BlindLoop-Difficulty-Feedback.imagevisual-question-answering10K<n<100K0 likes89 downloads20d agoHugging Face08mlfoundations-dev /seed_math_exploit_difficulty_annotationtabular100K<n<1M0 likes87 downloads2y agoHugging Face09CoffeeGitta /difficulty-E2H-AMC-generations Generations Dataset: E2H-AMC 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… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-E2H-AMC-generations.tabular10K<n<100K0 likes86 downloads4mo agoHugging Face10CoffeeGitta /difficulty-gsm8k-generations Generations Dataset: gsm8k 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… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-gsm8k-generations.tabular10K<n<100K0 likes73 downloads4mo agoHugging Face11LLParallax /difficulty-eval-64tabularn<1K0 likes71 downloads2mo agoHugging Face12CoffeeGitta /difficulty-aime_2025-generations Generations Dataset: aime_2025 Paper: LLMs Encode Their Failures: Predicting Success from Pre-Generation ActivationsCode: GitHub 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… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-aime_2025-generations.tabulartext-generationn<1K0 likes70 downloads1mo agoHugging Face13lego573402 /bible-vocabulary-difficulty Bible vocabulary-difficulty metrics, 12 translations Per-verse reading-difficulty metrics plus a cross-language book-name table keyed on USFM codes. Produced by bible-reader — see scripts/export_dataset.py. No verse text This dataset contains references and derived metrics only, never verse text. That is deliberate: it keeps translations under copyright (NASB) publishable as derived data, and it keeps the download small. Fetch the texts themselves from their own… See the full description on the dataset page: https://huggingface.co/datasets/lego573402/bible-vocabulary-difficulty.tabulartext-classification100K<n<1M0 likes68 downloads2mo agoHugging Face14RyanYr /dapo-math-17k-difficulty-qwen3-1.7b-base-k16 DAPO-Math-17k difficulty under Qwen3-1.7B-Base (K=16) For each of the 17,398 problems in the DAPO-Math-17k train set, how many of K=16 samples from the untrained base model are correct. The headline: 57.27% of problems are solved 0 out of 16 times, and not one problem is solved 16 out of 16. Difficulty here is entirely one-sided. Why count per problem instead of reporting mean accuracy In group-relative RL (GRPO and its relatives), a prompt group whose K responses… See the full description on the dataset page: https://huggingface.co/datasets/RyanYr/dapo-math-17k-difficulty-qwen3-1.7b-base-k16.tabular10K<n<100K0 likes66 downloads24d agoHugging Face15zhenghaoxu /R2E-Gym-Lite-with-Difficultytabular1K<n<10K5 likes57 downloads1y agoHugging Face16Seungjun /Korean-DeepMath-with-Difficulty Korean DeepMath with Difficulty This dataset enriches ChuGyouk/Korean-DeepMath with difficulty and topic metadata from zwhe99/DeepMath-103K. Join procedure Rows are matched using Korean-DeepMath[extra_info][index] -> original DeepMath row index. Added fields original_index difficulty topic Intended use Prepared for controlled Korean mathematical reasoning SFT experiments, including difficulty-aware sampling such as TDCS. No Easy /… See the full description on the dataset page: https://huggingface.co/datasets/Seungjun/Korean-DeepMath-with-Difficulty.tabulartext-generation100K<n<1M0 likes51 downloads27d agoHugging Face17ronantakizawa /japanese-text-difficulty Aozora Text Difficulty Dataset This dataset contains Japanese literary texts from the Aozora Bunko digital library, enhanced with jReadability-based difficulty analysis for Japanese language learning and curriculum development. Dataset Overview Source: Aozora Bunko (青空文庫) - Japan's premier digital library of public domain literature Enhancement: jReadability-based difficulty scoring using research-backed Japanese readability models Primary Methodology: jReadability - A… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/japanese-text-difficulty.tabulartext-classification1K<n<10K5 likes50 downloads10mo agoHugging Face18CoffeeGitta /difficulty-MATH-generations 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… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-MATH-generations.tabular10K<n<100K0 likes48 downloads4mo agoHugging Face19mlfoundations-dev /difficulty_filtering_seed_mathtabular10K<n<100K0 likes47 downloads2y agoHugging Face20lime-nlp /MATH_Difficulty Difficulty Estimation on MATH We annotate the entire MATH dataset with a difficulty score based on the performance of the Qwen 2.5-MATH-7B model. This provides an adaptive signal for curriculum construction and model evaluation. The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate… See the full description on the dataset page: https://huggingface.co/datasets/lime-nlp/MATH_Difficulty.tabular1M<n<10M0 likes45 downloads1y agoHugging Face21lime-nlp /orz_math_difficulty Difficulty Estimation on Open Reasoner Zero We annotate the entire Open Reasoner Zero dataset with a difficulty score based on the performance of the Qwen 2.5-MATH-7B model. This provides an adaptive signal for curriculum construction. Open Reasoner Zero is a curated a dataset of 57,000 reasoning-intensive problems used to train and evaluate reinforcement learning-based methods for large language models. Difficulty Scoring Method Difficulty scores are estimated using… See the full description on the dataset page: https://huggingface.co/datasets/lime-nlp/orz_math_difficulty.tabular1M<n<10M0 likes44 downloads1y agoHugging Face22agentlans /lime-nlp-difficulty lime-nlp Difficulty Estimation Math Datasets collection Unofficial reformatted version of lime-nlp/difficulty-estimation-math-datasets, which contains math problems and the Qwen 2.5 7B MATH model's success rates at solving those problems. The combined dataset has been split into 80% training and 20% testing data. Fields: row_id: the row number of each dataset entry, starting at 0 input: the math question from the dataset output: the correct answer (ground truth)… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/lime-nlp-difficulty.tabulartext-classification100K<n<1M0 likes42 downloads10mo agoHugging Face23argo11 /japanese-math-empirical-difficulty-pilot-50k Japanese Math Empirical Difficulty Pilot 50k This dataset is a 50,000-problem empirical difficulty pilot, not a full empirical labeling of the original 5.66M-row source dataset. It was created for LLM-jp experiment 0399, Team Victory SFT, to validate empirical difficulty label distribution, downstream split behavior, and the rollout/scoring pipeline before attempting labeling at the full 5.6M scale. Current Status This upload uses the v3 scorer with assistant-only… See the full description on the dataset page: https://huggingface.co/datasets/argo11/japanese-math-empirical-difficulty-pilot-50k.tabulartext-generation100K<n<1M0 likes41 downloads3mo agoHugging Face24agentlans /prompt-difficulty-model-ratings Prompt Difficulty Model Ratings Dataset contains approximately 100 000 ChatGPT prompts from agentlans/chatgpt The prompts were rated for difficulty using the large language models: allenai/Olmo-3-7B-Instruct google/gemma-3-12b-it ibm-granite/granite-4.0-h-tiny meta-llama/Llama-3.1-8B-Instruct microsoft/phi-4 mistralai/Ministral-3-8B-Instruct-2512nvidia/NVIDIA-Nemotron-Nano-9B-v2 Qwen/Qwen3-8B swiss-ai/Apertus-8B-Instruct-2509 tiiuae/Falcon-H1-7B-Instruct Prompt template:… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty-model-ratings.tabulartext-classification1M<n<10M0 likes38 downloads10mo agoHugging Face25agentlans /prompt-difficulty-mixed Prompt Difficulty Meta-Analysis Introduction The difficulty of large language model (LLM) prompts varies widely, from simple queries to complex multi-step reasoning tasks. This study develops a consistent, data-driven difficulty score for English ChatGPT prompts, using classifiers trained on labelled difficulty datasets. The goal is to improve automated prompt difficulty classification. Methods Detailed methods Several methods were used to quantify the… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty-mixed.tabulartext-classification10K<n<100K0 likes32 downloads10mo agoHugging Face26oscarfco /R2E-Gym-Lite-Difficulty-Clean-Onlytabular1K<n<10K0 likes31 downloads5mo agoHugging Face27ceyron /difficulty_and_receptive_field_advectionFinished run of te difficulty_and_receptive_field_advection_1d.ipynb example. tabularn<1K0 likes30 downloads2y agoHugging Face28drproduck /numinamath_verifiable_cleaned_wo_geo_mc_difficultytabular100K<n<1M0 likes30 downloads2y agoHugging Face29ronantakizawa /japanese-text-difficulty-2leveltabular1K<n<10K1 likes30 downloads1y agoHugging Face30mlfoundations-dev /difficulty_sorting_easy_seed_math_w_openthoughtstabular100K<n<1M0 likes28 downloads2y agoHugging Face

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