# Mathematical Derivation Graphs: A Relation Extraction Task in STEM Manuscripts

arXiv: https://arxiv.org/abs/2410.21324 · HTML: https://mlsanity.com/paper/2410.21324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://mlsanity.com/paper/2203.10316/) (2022)
- [Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](https://mlsanity.com/paper/2007.02387/) (2020)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle](https://mlsanity.com/paper/2407.13833/) (2024)
- [REFACTOR: Learning to Extract Theorems from Proofs](https://mlsanity.com/paper/2402.17032/) (2024)
- [SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model](https://mlsanity.com/paper/2404.13765/) (2024)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Testing in Automated Theorem Proving](https://mlsanity.com/paper/2604.23698/) (2026)

## Abstract (arXiv)

Recent advances in natural language processing (NLP), particularly with the emergence of large language models (LLMs), have significantly enhanced the field of textual analysis. However, while these developments have yielded substantial progress in analyzing natural language text, applying analysis to mathematical equations and their relationships within texts has produced mixed results. This paper takes the initial steps in expanding the problem of relation extraction towards understanding the dependency relationships between mathematical expressions in STEM articles. The authors construct the Mathematical Derivation Graphs Dataset (MDGD), sourced from a random sampling of the arXiv corpus, containing an analysis of 107 published STEM manuscripts with over 2000 manually labeled inter-equation dependency relationships, resulting in a new object referred to as a derivation graph that summarizes the mathematical content of the manuscript. The authors exhaustively evaluate analytical and machine learning (ML) based models to assess their capability to identify and extract the derivation relationships for each article and compare the results with the ground truth. The authors show that the best tested LLMs achieve F1 scores of ~45%-52%, and attempt to improve their performance by combining them with analytic algorithms and other methods.