Reverse Thinking Makes LLMs Stronger Reasoners

Arxiv Papers
2 Dec 202416:49

Summary

TLDRThe video explores the concept of reverse thinking in reasoning, highlighting its effectiveness in improving problem-solving accuracy. The *REvT-NK* framework is introduced, which enhances large language models (LLMs) by training them to reason both forward and backward. This bidirectional approach leads to significant performance improvements across a wide range of reasoning tasks, including common-sense and mathematical reasoning. The framework uses data augmentation and multitask learning, showing superior results compared to traditional methods, and excels in generalizing to new data sets with high sample efficiency.

Takeaways

  • πŸ˜€ Reverse thinking enhances problem-solving by applying both forward and backward reasoning.
  • πŸ˜€ Forward reasoning starts with a question and works toward an answer, while backward reasoning begins with the conclusion and traces back to the question.
  • πŸ˜€ Dual reasoning helps verify solutions and identify mistakes, improving accuracy in problem-solving.
  • πŸ˜€ The reev t n k framework introduces a method to foster reverse thinking in language models through data augmentation and new learning objectives.
  • πŸ˜€ The data augmentation process involves generating forward reasoning, backward questions, and backward reasoning through a teacher model, ensuring consistency and correctness.
  • πŸ˜€ Reev t n k trains models to think backward, reinforcing bidirectional reasoning and enhancing performance across various reasoning tasks.
  • πŸ˜€ Evaluation results show that reev t n k outperforms traditional methods, including zero-shot performance and symbolic knowledge distillation, by 6.84% to 13.53%.
  • πŸ˜€ Reev t n k is sample-efficient, outperforming other data augmentation methods even with just 10% of the training data.
  • πŸ˜€ The reev t n k method scales well with model size, with larger models achieving better results, surpassing models with more parameters in some cases.
  • πŸ˜€ Combining reev t n k with existing techniques like answer augmentation leads to greater performance improvements across multiple reasoning tasks.
  • πŸ˜€ The approach generalizes well to new datasets, demonstrating strong results on out-of-distribution tasks like openbook QA and buq.

Q & A

  • What is the main concept introduced in the script?

    -The main concept introduced in the script is 'reverse thinking,' which is a method that enhances reasoning by considering both forward and backward reasoning approaches. This dual reasoning method improves problem-solving accuracy in tasks such as math and logical reasoning.

  • How does forward reasoning differ from reverse reasoning?

    -Forward reasoning involves starting from the question and moving step by step towards the answer, while reverse reasoning begins with the predicted answer and traces back to the original question. Reverse reasoning helps verify solutions and detect contradictions.

  • What are the main research questions addressed by the framework 'Reev T N K'?

    -The main research questions addressed by 'Reev T N K' are whether reverse thinking can be applied to broader, less structured reasoning tasks and if a model can be trained to inherently think backward to improve its forward reasoning.

  • What is the role of data augmentation in 'Reev T N K'?

    -Data augmentation in 'Reev T N K' involves expanding the dataset by adding backward questions and backward reasoning. This augmented data helps the model develop backward reasoning skills, improving its ability to solve reasoning tasks by learning both forward and backward approaches.

  • What are the three main learning objectives for the student model in 'Reev T N K'?

    -The three main learning objectives are: (1) generating correct forward reasoning from the original question, (2) creating a backward question related to the original question, and (3) producing backward reasoning based on the generated backward question.

  • How does 'Reev T N K' compare to traditional knowledge distillation methods?

    -'Reev T N K' outperforms traditional knowledge distillation methods by enhancing performance through backward reasoning and data augmentation, leading to better accuracy and efficiency. It also achieves improvements with less data compared to symbolic knowledge distillation.

  • What were the results of the experiments comparing 'Reev T N K' with other methods?

    -The results showed that 'Reev T N K' consistently outperformed baseline models in multiple reasoning tasks, including common sense reasoning, math reasoning, and logical reasoning. It achieved an average improvement of 13.53% over zero-shot performance and 6.84% over symbolic knowledge distillation methods.

  • How does 'Reev T N K' demonstrate sample efficiency?

    -'Reev T N K' demonstrates sample efficiency by achieving better performance using only 10% of the training data compared to full training sets. This efficiency is particularly notable when compared to symbolic knowledge distillation methods, which require more data for similar performance.

  • What makes 'Reev T N K' scalable and adaptable to different model sizes?

    -'Reev T N K' scales effectively with model size, as demonstrated by its ability to outperform larger models (e.g., 176B) using a smaller 7B model. This indicates that the framework works efficiently even with smaller models, making it scalable and adaptable.

  • What types of reasoning tasks does 'Reev T N K' show the most improvement in?

    -'Reev T N K' shows the most improvement in medium to hard reasoning tasks, such as pre-algebra and pre-calculus. It excels in these areas by effectively leveraging both forward and backward reasoning to solve complex problems.

  • How does reverse reasoning improve the model's generalization ability?

    -Reverse reasoning enhances the model's generalization ability by allowing it to learn relationships between the original question and its backward version. This helps the model perform better on out-of-distribution datasets and improve accuracy on unseen tasks, which contributes to better overall adaptability.

  • What is the significance of combining 'Reev T N K' with other methods like answer augmentation?

    -Combining 'Reev T N K' with other methods, such as answer augmentation, leads to even greater performance improvements. This combination helps address various types of reasoning tasks more effectively, showing that 'Reev T N K' complements and enhances existing techniques.

Outlines

plate

This section is available to paid users only. Please upgrade to access this part.

Upgrade Now

Mindmap

plate

This section is available to paid users only. Please upgrade to access this part.

Upgrade Now

Keywords

plate

This section is available to paid users only. Please upgrade to access this part.

Upgrade Now

Highlights

plate

This section is available to paid users only. Please upgrade to access this part.

Upgrade Now

Transcripts

plate

This section is available to paid users only. Please upgrade to access this part.

Upgrade Now
Rate This
β˜…
β˜…
β˜…
β˜…
β˜…

4.7 / 5 (32 votes)

Related Tags
Reverse ThinkingLanguage ModelsReasoning EnhancementData AugmentationAI FrameworkBackward ReasoningMultitask LearningKnowledge DistillationMathematical ReasoningMachine LearningAI Research