аналитика - Mathematics - # Math Word Problems, Knowledge Distillation

Distilling Mathematical Expertise for Math Word Problems with Weak Supervision

Q: How can weak supervision methods reduce annotation costs effectively

Weak supervision methods can effectively reduce annotation costs by leveraging existing data that is easier and cheaper to obtain. Instead of requiring fully annotated datasets with "problem-equation" pairs, weakly supervised methods only need the problem statements and corresponding answers. By automating the process of generating equations from these inputs, weak supervision methods significantly lower the cost associated with manual annotation. This reduction in annotation costs is achieved through techniques such as automated rule-based searches, random walks, beam search, or combinatorial search strategies. These approaches allow for the extraction of mathematical knowledge without the need for labor-intensive human annotations.

Q: What are the implications of transferring knowledge from large language models to small models in other domains

The implications of transferring knowledge from large language models (LLMs) to small models extend beyond mathematical problem-solving into various domains. In natural language processing tasks like text generation, sentiment analysis, question-answering systems, and dialogue generation, transferring knowledge from LLMs can enhance model performance and capabilities. Large language models have a strong semantic understanding due to their vast pretraining on diverse text corpora. By distilling this knowledge into smaller models through techniques like knowledge distillation and refinement stages similar to those used in mathematical problem-solving contexts described in the provided context above), small models can benefit from improved accuracy and efficiency without requiring extensive computational resources.

Q: How can the concept of distillation and refinement be applied beyond mathematical problem-solving

The concept of distillation and refinement can be applied beyond mathematical problem-solving in various domains where complex information needs to be transferred efficiently between different scales or levels of complexity. For instance: Natural Language Processing: Distilling linguistic patterns learned by large transformer models into smaller ones could improve performance on tasks like machine translation or summarization. Computer Vision: Transferring visual features extracted by large convolutional neural networks (CNNs) to smaller architectures could enhance object recognition or image classification tasks. Healthcare: Applying distillation techniques to transfer medical diagnostic expertise learned by advanced AI systems to simpler healthcare tools could aid in accurate disease detection. Finance: Utilizing distilled financial market insights from sophisticated predictive models for risk assessment purposes using more resource-efficient algorithms. By adapting the principles of distillation and refinement across different domains, organizations can leverage valuable insights captured by complex AI systems while maintaining scalability and efficiency in their applications or services.

Основные понятия

Innovative two-stage framework transfers mathematical expertise from large to tiny models for improved performance on Math Word Problems.

Аннотация

Addressing the challenge of high annotation costs in solving Math Word Problems (MWPs) through weakly supervised settings.
Introducing a two-stage framework that transfers mathematical expertise from Large Language Models (LLMs) to small models.
Distillation Stage focuses on extracting knowledge from LLMs to construct problem-equation pairs.
Refinement Stage utilizes unsuccessfully searched data effectively to enhance model performance.
Training a small model using distilled data shows improved performance on datasets while maintaining lower computational costs than ChatGPT.

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Статистика

Recent works have proposed weakly supervised task settings that rely solely on the final answer as a supervised signal.
Large Language Models like ChatGPT have opened up new possibilities for addressing MWPs directly.
The rise of LLMs introduces computational demands that make them less ideal for resource-tight settings.

Цитаты

"We propose a novel weakly supervised method that leverages ChatGPT to assist in searching for 'problem-equation' pairs equations."
"Our method fully leverages the semantic understanding capabilities during the searching 'problem-equation' pair."
"Our small model exhibits superior performance compared to zero-shot LLM."

Ключевые выводы из

From Large to Tiny

by Qingwen Lin,... в arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14390.pdf

Дополнительные вопросы

How can weak supervision methods reduce annotation costs effectively

Weak supervision methods can effectively reduce annotation costs by leveraging existing data that is easier and cheaper to obtain. Instead of requiring fully annotated datasets with "problem-equation" pairs, weakly supervised methods only need the problem statements and corresponding answers. By automating the process of generating equations from these inputs, weak supervision methods significantly lower the cost associated with manual annotation. This reduction in annotation costs is achieved through techniques such as automated rule-based searches, random walks, beam search, or combinatorial search strategies. These approaches allow for the extraction of mathematical knowledge without the need for labor-intensive human annotations.

What are the implications of transferring knowledge from large language models to small models in other domains

The implications of transferring knowledge from large language models (LLMs) to small models extend beyond mathematical problem-solving into various domains. In natural language processing tasks like text generation, sentiment analysis, question-answering systems, and dialogue generation, transferring knowledge from LLMs can enhance model performance and capabilities. Large language models have a strong semantic understanding due to their vast pretraining on diverse text corpora. By distilling this knowledge into smaller models through techniques like knowledge distillation and refinement stages similar to those used in mathematical problem-solving contexts described in the provided context above), small models can benefit from improved accuracy and efficiency without requiring extensive computational resources.

How can the concept of distillation and refinement be applied beyond mathematical problem-solving

The concept of distillation and refinement can be applied beyond mathematical problem-solving in various domains where complex information needs to be transferred efficiently between different scales or levels of complexity. For instance:

Natural Language Processing: Distilling linguistic patterns learned by large transformer models into smaller ones could improve performance on tasks like machine translation or summarization.
Computer Vision: Transferring visual features extracted by large convolutional neural networks (CNNs) to smaller architectures could enhance object recognition or image classification tasks.
Healthcare: Applying distillation techniques to transfer medical diagnostic expertise learned by advanced AI systems to simpler healthcare tools could aid in accurate disease detection.
Finance: Utilizing distilled financial market insights from sophisticated predictive models for risk assessment purposes using more resource-efficient algorithms.
By adapting the principles of distillation and refinement across different domains, organizations can leverage valuable insights captured by complex AI systems while maintaining scalability and efficiency in their applications or services.