Easy Interpreter: Latest Research: Mathematical Reasoning of Large Language Models

The research paper, GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models, investigates how well large language models (LLMs) solve math problems. The researchers used the GSM8K benchmark, a test that includes grade-school math questions, to assess these models' math abilities.

Key Findings

• LLM Reasoning Fragility: Models struggle with small changes in questions, especially when numbers or distracting details are added. Extra sentences, even irrelevant ones, often confuse these models.
• GSM-Symbolic Benchmark: To address this, the researchers created GSM-Symbolic, a tool that generates varied math problems, showing that model performance drops when only the numbers are changed.
• Impact of Complexity: As question complexity rises, models’ performance worsens, suggesting they rely on pattern matching rather than genuine reasoning.
• GSM-NoOp: GSM-NoOp, another test, adds irrelevant information to the questions. Many models mistakenly include these irrelevant details, revealing a lack of true logical understanding.

Conclusion

The study concludes that current LLMs rely on pattern recognition rather than true problem-solving skills, stressing the need for benchmarks that foster genuine reasoning development. (Mirzadeh, I., et al. 2024. GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. arXiv preprint arXiv:2410.05229).