Challenging Large Language Models with Adversarial Math Problems

Core Concepts

The author explores generating math problems that challenge large language models, aiming to assess students' true problem-solving abilities in the presence of advanced AI tools.

Abstract

The content discusses creating adversarial examples for math word problems to test large language models. By changing numeric values while maintaining coherence and difficulty, the method significantly degrades LLMs' performance. The study investigates shared vulnerabilities among LLMs and proposes cost-effective attack strategies. Large language models (LLMs) have transformed education, raising concerns about evaluating students fairly in their presence. Efforts like plagiarism detection struggle with LLM-generated content. Adversarial attacks on LLMs aim to generate unsolvable math problems while preserving original structure and difficulty. By editing numeric values in math word problems, the method challenges LLMs without altering coherence or complexity. Experiments show a significant decrease in LLMs' math problem-solving ability using adversarial examples. The study introduces a new paradigm for fair evaluation in education by creating unsolvable math problems for large language models (LLMs). By leveraging abstract syntax trees, the method generates adversarial examples that degrade LLMs' performance while maintaining original problem structure and difficulty. The research identifies vulnerabilities among LLMs and proposes cost-effective strategies to attack high-cost models.

Stats

Recent advances in large language models have revolutionized education. Plagiarism detection tools struggle with identifying content generated by machines. Adversarial attacks focus on modifying prompts to cause incorrect outputs from LLMs. Changing numeric values in math word problems significantly impacts LLM performance. Different generation methods affect model accuracy differently.

Quotes

"Efforts like plagiarism detection exist but are limited in identifying LLM-generated content." "Our aim is not just to challenge LLMs but to do so reflecting real-world educational standards." "The more restrictive a method is, the closer the difficulty levels are between adversarial examples and original problems."

Key Insights Distilled From

LLM-Resistant Math Word Problem Generation via Adversarial Attacks

by Roy Xie,Chen... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17916.pdf

Deeper Inquiries

How can educators ensure fair evaluation for students amidst advancements in large language models?

Educators can ensure fair evaluation for students by implementing a combination of strategies. Firstly, they can focus on assessing not just the final answer but also the problem-solving process. By emphasizing critical thinking skills and understanding concepts rather than rote memorization, educators can gauge a student's true abilities. Additionally, incorporating diverse assessment methods such as practical applications, projects, and discussions can provide a more comprehensive view of a student's capabilities beyond what an AI system may assess.

What ethical considerations should be taken into account when designing challenges for AI systems?

When designing challenges for AI systems like those discussed in the study, several ethical considerations must be prioritized. One key consideration is ensuring that the challenges do not exacerbate educational inequalities or disadvantage individuals with limited access to technology or resources. It is essential to maintain fairness and equity in assessments while avoiding unintentional biases that could impact certain groups negatively. Furthermore, transparency about the use of AI systems in evaluations is crucial to uphold trust and integrity within educational settings. Educators should also consider potential unintended consequences of challenging these systems and strive to mitigate any negative impacts on students' learning experiences.

How might the findings of this study impact future developments in educational technology?

The findings of this study could have significant implications for future developments in educational technology. By highlighting vulnerabilities in large language models (LLMs) when solving math problems, educators and developers may explore ways to enhance these models' robustness against adversarial attacks. Additionally, insights from this research could lead to the creation of more sophisticated anti-plagiarism tools tailored specifically for LLM-generated content. This could help address concerns related to academic dishonesty facilitated by advanced natural language generation capabilities. Moreover, understanding how LLMs interact with math word problems opens up avenues for improving personalized learning experiences through adaptive technologies that cater to individual students' needs based on their problem-solving abilities identified through interactions with LLMs.