Curation of resources for LLM mathematical reasoning, most of which are screened by @tongyx361 to ensure high quality and accompanied with elaborately-written concise descriptions to help readers get the gist as quickly as possible.

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✨ Featured by:

• Probably the most comprehensive list of training prompt&answer datasets for complex mathematical QA tasks across the web.
• Lists of the series of work implementing the current open-source SotA for mathematical problem-solving tasks.

The following resources are listed in (roughly) chronological order of publication.

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The following list mainly focuses (training) datasets for complex mathematical QA, leaving out most basic arithmetic datasets like AQuA-RAT, NumGLUE, MathQA, ASDiv, etc. For more other datasets, you can refer to the tables at the end of A Survey of Deep Learning for Mathematical Reasoning.

• MATH/Train
  • # of Queries: 7,500
  • Query Description:
    • "The MATH dataset consists of problems from mathematics competitions including the AMC 10, AMC 12, AIME, and more."
    • "To provide a rough but informative comparison to human-level performance, we randomly sampled 20 problems from the MATH test set and gave them to humans. We artificially require that the participants have 1 hour to work on the problems and must perform calculations by hand. All participants are university students.
      • One participant who does not like mathematics got 8/20 = 40% correct.
      • A participant ambivalent toward mathematics got 13/20.
      • Two participants who like mathematics got 14/20 and 15/20.
      • A participant who got a perfect score on the AMC 10 exam and attended USAMO several times got 18/20.
      • A three-time IMO gold medalist got 18/20 = 90%, though missed questions were exclusively due to small errors of arithmetic."
    • "Problems span various subjects and difficulties. The seven subjects are Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, and Precalculus."
    • "While subjects like Prealgebra are generally easier than Precalculus, within a subject problems can take on different difficulty levels. We encode a problem’s difficulty level from ‘1’ to ‘5,’ following AoPS. A subject’s easiest problems for humans are assigned a difficulty level of ‘1,’ and a subject’s hardest problems are assigned a difficulty level of ‘5.’ Concretely, the first few problems of an AMC 8 exam are often level 1, while AIME problems are level 5."
  • Answer Description:
    • Solutions and final answers are by experts from the AOPS community.
    • "Problems and solutions are consistently formatted using LaTeX and the Asymptote vector graphics language."
• AMPS/Khan-Academy
  • # of Queries: 103,059
  • Query Description:
    • "The Khan Academy subset of AMPS has 693 exercise types with over 100,000 problems and full solutions.
    • Problem types range from elementary mathematics (e.g. addition) to multivariable calculus (e.g. Stokes’ theorem), and are used to teach actual K-12 students.
    • The exercises can be regenerated using code from github.com/Khan/khan-exercises/."
  • Answer Description: Same as query.
• AMPS/Mathematica
  • # of Queries: 4,830,500
  • Query Description:
    • "With Mathematica, we designed 100 scripts that test distinct mathematics concepts, 37 of which include full step-by-step LaTeX solutions in addition to final answers. We generated around 50,000 exercises from each of our scripts, or around 5 million problems in total."
    • "Problems include various aspects of algebra, calculus, counting and statistics, geometry, linear algebra, and number theory."
  • Answer Description: Same as query.
• GSM8K/Train
  • # of Queries: 7,473
  • Query Description:
    • "GSM8K consists of 8.5K high quality grade school math problems created by human problem writers."
    • These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations ( − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem.
    • "We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection."
  • Answer Description:
    • Written by labelers from Surge AI (main) and Upwork (auxilliary)
    • "After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors."
• TAL-SCQ5K-EN/Train
  • # of Queries: 3,000
  • Query Description:
    • "TAL-SCQ5K-EN/TAL-SCQ5K-CN are high quality mathematical competition datasets in English and Chinese language created by TAL Education Group, each consisting of 5K questions(3K training and 2K testing).
    • The questions are in the form of multiple-choice
    • and cover mathematical topics at the primary,junior high and high school levels."
  • Answer Description: Same as query.
• TAL-SCQ5K-CN/Train
  • # of Queries: 3,000
  • Query Description: Similar to TAL-SCQ5K-EN/Train.
  • Answer Description: Same as query.
• CAMEL-Math
  • # of Queries: 50,000
  • Query Description:
    • "Math dataset is composed of 50K problem-solution pairs obtained using GPT-4.
    • The dataset problem-solution pairs generating from 25 math topics, 25 subtopics for each topic and 80 problems for each "topic,subtopic" pairs."
  • Answer Description: Same as query.
• MetaMathQA-GSM8K/SV
  • # of Queries: 29,283
  • Query Description:
    • "In Self-Verification (SV), the question with the answer is first rewritten into a declarative statement, e.g., "How much did he pay?" (with the answer 110) is rewritten into "He paid $10". Then, a question for asking the value of $x$ is appended, e.g., "What is the value of unknown variable $x$?"."
  • Answer Description: Same as query.
• MetaMathQA-GSM8K/FOBAR
  • # of Queries: 16,503
  • Query Description:
    • "FOBAR proposes to directly append the answer to the question, i.e., "If we know the answer to the above question is $a_{i}^*$ , what is the value of unknown variable $x$?""
  • Answer Description: Same as query.
• MetaMathQA-MATH/SV
  • # of Queries: 4,596
  • Query Description: Similar to MetaMath-GSM8K/SV.
  • Answer Description: Same as query.
• MetaMathQA-MATH/FOBAR
  • # of Queries: 4,911
  • Query Description: Similar to MetaMath-GSM8K/FOBAR.
  • Answer Description: Same as query.
• MathInstruct/College-Math
  • # of Queries: 1,840
  • Query Description:
    • "We use GPT-4 to …
    • create question-CoT pairs through Self-Instruct (Wang et al., 2023h),
    • utilizing a few seed exemplars found online."
  • Answer Description: Same as query.
• MMIQC/AugSimilar
  • # of Queries: (unspecified)
  • Query Description:
    • "In our practice, we find that GPT tends to generate several almost the same problems regardless of the given seed problem when asked to generate up to 10 new problems. Thus, we only ask GPT to generate 3 problems (with a solution, for rejection sampling) each time".
    • Considering rejection sampling needs the answer to the problem better to be correct, we use the stronger GPT-4(-1106) instead of GPT-3.5.
    • To control the cost, our prompt emphasizes that the solution should be as brief as possible.
    • "We use a temperature $T = 1.0$ for both procedures."
  • Answer Description: Same as query.
• MMIQC/IQC
  • # of Queries: (unspecified)
  • Query Description:
    • "Our approach, termed IQC (Iterative Question Composing), deviates from this by iteratively constructing more complex problems. It augments the initial problems, adding additional reasoning steps without altering their intrinsic logical structure."
    • "We perform Iterative Question Composing for 4 iterations".
    • "We note that although some of the questions are not rigorously a sub-problem or sub-step of the corresponding problem in the previous iteration as required in our prompt, they are still valid questions that can increase the diversity of the dataset."
    • "Specifically, we use GPT-4(-1106) for question composing model $\pi_{q}$ with a $T = 0.7$ temperature."
  • Answer Description: Same as query.
• MMIQC/MathStackExchange
  • # of Queries: 1,203,620
  • Query Description:
    • "we extract the data collected from Mathematics Stack Exchange in RedPajama (Computer, 2023) and pre-process it into question-response pairs.
    • For each Mathematics Stack Exchange page, we only retain the answer ranked first by RedPajama.
    • Then we filter out the answer that does not contain a formula environment symbol ‘$’."
  • Answer Description: No short final answers.
• MWPBench/CollegeMath/Train
  • # of Queries: 1,281
  • Query Description:
    • "We curated a collection of nine college mathematics textbooks, each addressing a distinct topic.
    • These textbooks encompass seven critical mathematical disciplines: algebra, pre-calculus, calculus, vector calculus, probability, linear algebra, and differential equations.
    • These textbooks are originally in PDF format and we convert them to text format using the MathPix API, where equations are transformed to LaTeX format. Once converted a textbook to text format, we are ready to extract exercises and their solutions. For each book, we first manually segment the book into chapter and identify pages with exercises and their solutions. Then we extract questions in exercises and their associated short answers."
  • Answer Description: Same as query.
• AOPS
  • # of Queries: 3,886
  • Query Description:
    • Problems from mathematical competitions of AIME AMC≤2023, crawled from AOPS by @yulonghui .
  • Answer Description: Same as query.
• WebInstruct(Sub)
  • # of Queries: ~10M(2,335,220)
  • Query Description:
    • "In this paper, we aim to mine these instruction-response pairs from the web using a three-step pipeline.
      1. Recall step: We create a diverse seed dataset by crawling several quiz websites. We use this seed data to train a fastText model (Joulin et al., 2016) and employ it to recall documents from Common Crawl (Computer, 2023). GPT-4 is used to trim down the recalled documents by their root URL. We obtain 18M documents through this step.
      2. Extract step: We utilize open-source LLMs like Qwen-72B (Bai et al., 2023) to extract Q-A pairs from these documents, producing roughly 5M candidate Q-A pairs.
      3. Refine step: After extraction, we further employ Mixtral-8×22B (Jiang et al., 2024) and Qwen-72B (Bai et al., 2023) to refine (Zheng et al., 2024b) these candidate Q-A pairs. This refinement operation aims to remove unrelated content, fix formality, and add missing explanations to the candidate Q-A pairs. Eventually, we harvest a total of 10M instruction-response pairs through these steps."
    • "The pie chart reveals that WebInstruct is predominantly composed of science-related subjects, with 81.69% of the data falling under the broad "Science" category.
      • Within this category, Mathematics takes up the largest share at 68.36%,
      • followed by Physics, Chemistry, and Biology.
    • The remaining non-science categories, such as Business, Art & Design, and Health & Medicine, contribute to the diversity of the dataset."
    • "In terms of data sources, the vast majority (86.73%) of the instruction-response pairs come from exam-style questions, while forum discussions make up the remaining 13.27%."
    • "To quantify the error percentages, we randomly sample 50 refined QA examples and ask the human annotators to compare whether the refined examples are correct and significantly better than the extracted ones in terms of format and intermediate solutions.
      • As we can see from Figure 6, 78% examples have been improved after refinement
      • and only 10% examples introduce hallucinations after refinement."
  • Answer Description: Same as query.

• Llemma & Proof-Pile-2: Open-sourced re-implementation of Minerva.
  • Open-sourced corpus Proof-Pile-2 comprising 51.9B tokens (by DeepSeek tokenizer).
  • Continually pre-trained based on CodeLLaMAs.
• OpenWebMath:
  • 13.6B tokens (by DeepSeek tokenizer).
  • Used by Rho-1 to achieve performance comparable with DeepSeekMath.
• MathPile:
  • 8.9B tokens (by DeepSeek tokenizer).
  • Mainly comprising arXiv papers.
  • Shown not effective (on 7B models) by DeepSeekMath.
• DeepSeekMath: Open-sourced SotA (as of 2024-04-18).
  • Continually pre-trained based on DeepSeek-LLMs and DeepSeekCoder-7B
• Rho-1: Selecting tokens based on loss/perplexity, achieving performance comparable with DeepSeekMath but only based on 15B OpenWebMath corpus.

• RFT: SFT on rejection-sampled model outputs is effective.
• MetaMath: Constructing problems of ground truth answer (but no necessarily feasible) by self-verification.
  • Augmenting with GPT-3.5-Turbo.
• AugGSM8k : Common data augmentation on GSM8k helps little in generalization to MATH.
• MathScale: Scaling synthetic data to ~2M samples using GPT-3.5-Turbo with knowledge graph.
• KPMath: Scaling synthetic data to 1.576M samples using GPT-4-Turbo with knowledge graph.
• XWin-Math: Simple scaling synthetic data to 480k MATH 960k GSM8k samples using GPT-4-Turbo with knowledge graph.

• MAmmoTH: SFT on CoT&PoT-mixing data is effective.
• ToRA & MARIO: The fisrt open-sourced model works to verify the effectiveness of SFT for tool-integrated reasoning.
• OpenMathInstruct-1: Scaling synthetic data to 1.8M using Mixtral-8x7B
• AlphaMath: Use MCTS to synthesize tool-integrated reasoning paths and step-level reward labels, then train the model with a multi-task language model and reward model loss to get a policy-and-value model.
  • Compared with DeepSeekMath-7B-RL (58.8% pass@1) on MATH, AlphaMath catches up by merely SFT DeepSeekMath-7B with MARIO and AlphaMath and further improves to 68.6% with Step-level Beam Search (SBS) decoding. (Table 4)

• Math-Shepherd: Consturcting step-correctness labels based on an MCTS-like method.

• DUP: Prompting the model with three-stage Deeply Understand the Problem prompts, which comprises 1) core question extraction 2) problem-solving information extraction and 3) CoT reasoning, improving more than Plan-and-Solve and Least-to-Most prompting on simple arithmetic, commonsense and symbolic reasoning tasks.

Here we focus on several the most important benchmarks.

• MMLU(-Math): Measuring Massive Multitask Language Understanding, reference: https://arxiv.org/abs/2009.03300, https://github.com/hendrycks/test, MIT License
• MATH: Measuring Mathematical Problem Solving With the MATH Dataset, reference: https://arxiv.org/abs/2103.03874, https://github.com/hendrycks/math, MIT License
• MGSM: Multilingual Grade School Math Benchmark (MGSM), Language Models are Multilingual Chain-of-Thought Reasoners, reference: https://arxiv.org/abs/2210.03057, https://github.com/google-research/url-nlp, Creative Commons Attribution 4.0 International Public License (CC-BY)

• miniF2F: “a formal mathematics benchmark (translated across multiple formal systems) consisting of exercise statements from olympiads (AMC, AIME, IMO) as well as high-school and undergraduate maths classes”.
• OlympiadBench: “an Olympiad-level bilingual multimodal scientific benchmark”.
  • GPT-4V attains an average score of 17.23% on OlympiadBench, with a mere 11.28% in physics.

• GitHub - lupantech/dl4math: Resources of deep learning for mathematical reasoning (DL4MATH).

• AIMO: “a new $10mn prize fund to spur the open development of AI models capable of performing as well as top human participants in the International Mathematical Olympiad (IMO)”.