Autonomous Data Selection with Language Models Enhances Mathematical Reasoning in Language Models

Temel Kavramlar

Leveraging base language models with meta-prompts for zero-shot verification, the proposed Autonomous Data Selection (AutoDS) method autonomously evaluates and selects high-quality mathematical content, enabling efficient continual pretraining of language models and substantial improvements in downstream mathematical reasoning tasks.

Özet

The paper introduces a novel strategy, Autonomous Data Selection (AutoDS), that utilizes base language models equipped with meta-prompts for zero-shot verification and autonomous evaluation of mathematical content. This approach aims to address the shortage of labeled high-quality mathematical training resources for language models.

Key highlights:

• The AutoDS method employs a scoring function derived from language model logits to quantitatively assess the mathematical quality and educational value of textual content, without the need for human-annotated data or trained classifiers.
• The authors introduce the open-source AutoMathText dataset, a comprehensive collection of mathematical content from sources like Common Crawl, arXiv, and GitHub, to enrich AI model training.
• Empirical results demonstrate that continually pretraining a 7B-parameter Mistral language model on the AutoMathText dataset, selected using the AutoDS method, leads to substantial improvements in downstream performance on complex mathematical reasoning tasks (MATH, GSM8K, BIG-Bench Hard) with a token amount reduced by orders of magnitude compared to previous works.
• The AutoDS approach showcases a 2 times increase in pretraining token efficiency compared to state-of-the-art baselines, highlighting the potential of this method in enhancing models' mathematical reasoning capabilities.

İstatistikler

"To improve language models' proficiency in mathematical reasoning via con-tinual pretraining, we introduce a novel strategy that leverages base language models for autonomous data selection."
"Empirical results demonstrate that continually pretraining a 7B-parameter Mistral language model on the AutoMathText dataset, selected using the AutoDS method, leads to substantial improvements in downstream performance on complex mathematical reasoning tasks (MATH, GSM8K, BIG-Bench Hard) with a token amount reduced by orders of magnitude compared to previous works."
"The AutoDS approach showcases a 2 times increase in pretraining token efficiency compared to state-of-the-art baselines, highlighting the potential of this method in enhancing models' mathematical reasoning capabilities."

Alıntılar

"Leveraging base language models with meta-prompts for zero-shot verification, the proposed Autonomous Data Selection (AutoDS) method autonomously evaluates and selects high-quality mathematical content, enabling efficient continual pretraining of language models and substantial improvements in downstream mathematical reasoning tasks."
"The AutoDS approach showcases a 2 times increase in pretraining token efficiency compared to state-of-the-art baselines, highlighting the potential of this method in enhancing models' mathematical reasoning capabilities."

Önemli Bilgiler Şuradan Elde Edildi

Autonomous Data Selection with Language Models for Mathematical Texts

by Yifan Zhang,... : arxiv.org 04-03-2024

https://arxiv.org/pdf/2402.07625.pdf

Daha Derin Sorular

How can the AutoDS method be extended to other specialized domains beyond mathematics, such as scientific literature or legal texts, to enhance language models' capabilities in those areas

The AutoDS method can be extended to other specialized domains beyond mathematics by adapting the scoring function and meta-prompts to suit the specific characteristics of those domains. For scientific literature, the scoring function could be tailored to evaluate the accuracy of scientific claims or the relevance of research findings. Meta-prompts could be designed to assess the novelty and impact of research articles. In legal texts, the scoring function could focus on the coherence of legal arguments and the adherence to legal principles. Meta-prompts could be formulated to evaluate the applicability of legal precedents or the strength of legal reasoning. By customizing the scoring function and meta-prompts, the AutoDS method can effectively select high-quality data in various specialized domains, enhancing language models' proficiency in those areas.

What are the potential limitations or biases that may arise from the autonomous data selection approach, and how can they be mitigated

One potential limitation of the autonomous data selection approach is the risk of reinforcing existing biases present in the training data. Language models may inadvertently learn and perpetuate biases present in the data they are trained on, leading to biased outputs and decisions. To mitigate this risk, it is essential to regularly audit the selected data for biases and ensure diversity and representativeness in the training data. Additionally, incorporating fairness metrics and bias detection algorithms during the data selection process can help identify and address biases proactively. Transparency in the data selection process, including documenting the criteria used for selection and the evaluation of data quality, can also help mitigate biases and promote accountability in the model training process.

Given the emphasis on mathematical reasoning, how could the insights from this work be applied to the development of specialized mathematical reasoning agents or tutoring systems

The insights from this work on mathematical reasoning can be applied to the development of specialized mathematical reasoning agents or tutoring systems by leveraging the autonomous data selection approach to curate high-quality mathematical content for training these systems. By continuously pretraining language models on curated mathematical datasets using the AutoDS method, specialized mathematical reasoning agents can enhance their problem-solving skills, logical reasoning abilities, and mathematical comprehension. These agents can be designed to assist students in solving complex math problems, providing step-by-step explanations, and offering personalized feedback based on the mathematical content they have been trained on. Additionally, the autonomous evaluation capabilities of the language models can be integrated into tutoring systems to assess students' mathematical understanding and provide tailored learning experiences. This approach can revolutionize the field of math education by offering personalized and effective support to learners at various levels of mathematical proficiency.