Summary of Can Llms Master Math? Investigating Large Language Models on Math Stack Exchange, by Ankit Satpute and Noah Giessing and Andre Greiner-petter and Moritz Schubotz and Olaf Teschke and Akiko Aizawa and Bela Gipp
Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange
by Ankit Satpute, Noah Giessing, Andre Greiner-Petter, Moritz Schubotz, Olaf Teschke, Akiko Aizawa, Bela Gipp
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved impressive results in natural language tasks, but their capabilities in mathematics are a different story. This study investigates the proficiency of LLMs in answering mathematical questions using a two-step approach. The researchers employed top-performing LLMs on math question-answer benchmarks to generate answers for 78 questions from the Math Stack Exchange (MSE). They then conducted a case analysis on the best-performing model, focusing on the quality and accuracy of its answers through manual evaluation. The results show that GPT-4 performs well, outperforming current approaches on ArqMATH3 Task1, but it still struggles with accurately answering all questions. This paper highlights the limitations of LLMs in navigating complex mathematical problem-solving and sets the stage for future research and advancements in AI-driven mathematical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computers can answer math questions. Even though computers are great at understanding language, they struggle with math because it requires a lot of precision. The researchers used special computer programs called Large Language Models to try to answer 78 math questions from the internet. They found that one program, GPT-4, did really well and was even better than other approaches. However, it still didn’t get all the answers right. This study shows how far computers have come but also what they’re not good at yet. It’s like a challenge for computer scientists to make computers better at math. |
Keywords
» Artificial intelligence » Gpt » Mse » Precision
