Challenging Large Language Models with Adversarial Math Problems

Основні поняття

The author explores generating math word problems that challenge large language models, aiming to degrade their problem-solving abilities significantly while maintaining the original difficulty and coherence of the questions.

Анотація

The content discusses a novel approach to creating math word problems that are unsolvable by large language models (LLMs) to ensure fair evaluation in education. By leveraging abstract syntax trees, the method generates adversarial examples that cause LLMs to produce incorrect answers by editing numeric values in the problems. The study evaluates various LLMs, proposing a cost-effective approach to attacking high-cost models and conducting human evaluations on the generated adversarial examples.

Key points include:

• Introduction of a new paradigm for fair evaluation in education.
• Use of abstract syntax trees to generate unsolvable math word problems for LLMs.
• Evaluation of different LLMs' performance under adversarial settings.
• Proposal of a cost-effective method for attacking expensive API-based models.
• Human evaluation results indicating correctness, coherence, and similarity of generated problems.

The study aims to shed light on educational tool development and ethical use of LLMs in education.

Статистика

We conduct experiments on 7 open-source models: MetaMath 7B, Mistral 7B, Llama-2 13B, WizardMath 13B, Vicuna 13B, CodeLlama 34B, MetaMath 70B.
We also evaluate 2 closed-source models: GPT-4-Turbo and GPT-3.5-Turbo.

Цитати

"Generating adversarial examples which preserve the structure and difficulty of the original questions aimed for assessment."
"Focusing on math word problems to structurally generate adversarial examples causing LLMs to produce incorrect answers."

Ключові висновки, отримані з

LLM-Resistant Math Word Problem Generation via Adversarial Attacks

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

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

Глибші Запити

How can this method be adapted for other educational domains beyond math?

The method of generating adversarial examples to challenge LLMs can be adapted for various educational domains beyond math by modifying the approach to suit the specific characteristics of each domain. For subjects like science, history, or language arts, similar abstract syntax tree (AST) structures could be used to systematically generate adversarial examples that test the understanding and reasoning abilities of LLMs in those areas. The key lies in identifying the unique features and constraints of each subject domain and translating them into rules for generating valid adversarial examples.

What potential ethical implications arise from challenging LLMs with unsolvable problems?

Challenging LLMs with unsolvable problems raises several ethical considerations. One major concern is the potential exacerbation of educational inequality. By creating problems specifically designed to deceive LLMs, individuals or institutions without access to such advanced tools may face a disadvantage in academic evaluations. This could widen the gap between those with technological resources and those without, leading to unfair assessments and reinforcing existing disparities.
Additionally, there are concerns about the impact on students' learning experiences. If educational tools rely heavily on LLM-generated content but fail to accurately assess students' true problem-solving abilities due to these challenges, it could undermine the integrity of education outcomes and hinder genuine skill development.
Furthermore, there is a risk of unintentionally promoting unethical behavior among students if they learn that bypassing assessments using machine-generated solutions is possible. This could erode academic integrity and devalue authentic learning efforts.

How might this research impact the future development of educational tools using large language models?

This research has significant implications for shaping the future development of educational tools utilizing large language models (LLMs). By highlighting vulnerabilities in current assessment methods involving LLMs, it underscores the importance of ensuring fair evaluation practices in an increasingly AI-driven educational landscape.
One direct impact could be improvements in anti-plagiarism measures tailored specifically for detecting machine-generated content within student submissions. Educational technology developers may need to enhance plagiarism detection algorithms to differentiate between human-authored work and outputs generated by sophisticated language models.
Moreover, this research emphasizes the need for ongoing discussions around ethical AI use in education. It may lead to guidelines or frameworks being established regarding how educators should leverage LLM capabilities while maintaining fairness and authenticity in assessments.
Overall, this study prompts a reevaluation of how we integrate AI technologies like LLMs into education settings responsibly while upholding academic standards and fostering genuine learning outcomes.