Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
Adaptive-Consistency method reduces sampling budget while maintaining high accuracy, with an average accuracy drop of less than 0.1%.
Key Findings
Methodology
Adaptive-Consistency is a method that dynamically adjusts the number of samples using a Dirichlet distribution to model sample probability distribution, employing a lightweight stopping criterion to decide whether to continue sampling. This model-agnostic approach is applicable to various pre-trained language models.
Key Results
- On CODE-DAVINCI-002, Adaptive-Consistency reduced the number of samples by 3.4 times with no drop in accuracy.
- On VICUNA-13B, it required 1.9 times fewer samples with almost no change in accuracy.
- On GPT-3.5-TURBO, it sampled 4.4 times fewer samples with less than 0.2% drop in accuracy.
Significance
This study significantly reduces the computational cost of large language models while maintaining output quality through the Adaptive-Consistency method. It demonstrates superior performance in reasoning and code generation tasks, especially in high-stakes applications requiring extensive sampling.
Technical Contribution
Adaptive-Consistency introduces a dynamic sampling strategy, breaking the fixed budget limitation of traditional self-consistency methods, providing a more flexible and efficient sampling mechanism applicable to various tasks and models.
Novelty
Adaptive-Consistency is the first to introduce a Dirichlet distribution-based dynamic sampling strategy in large language models, significantly improving sampling efficiency compared to traditional methods.
Limitations
- In complex tasks, although the number of samples is reduced, higher computational resources may be needed to evaluate the stopping criterion.
- For tasks with high uncertainty, the stopping criterion may need further optimization.
Future Work
Future research can explore different stopping criteria and more complex models to further enhance sampling efficiency and accuracy.
AI Executive Summary
Balancing accuracy and computational cost in large language models has been a challenge. Traditional self-consistency methods improve output correctness by generating a fixed number of samples, but this approach is computationally expensive. Adaptive-Consistency introduces a dynamic sampling strategy, significantly reducing computational costs while maintaining output quality.
The core of Adaptive-Consistency lies in its lightweight stopping criterion, which models the probability distribution of samples using a Dirichlet distribution to dynamically adjust the number of samples per question. Experimental results show that this method performs excellently across various datasets and models, particularly in code generation and reasoning tasks.
While Adaptive-Consistency excels in reducing sampling budgets, the computation of the stopping criterion may require additional resources in complex tasks. Future research can explore more efficient stopping criteria and more complex models to further enhance the method's performance.
Deep Analysis
Background
In recent years, large language models have been widely applied in tasks such as text generation, reasoning, and code generation. However, as model size and complexity increase, so do sampling time and computational costs. Self-consistency methods improve accuracy by generating multiple samples and selecting the most frequent output, but this approach is computationally expensive.
Core Problem
The fixed budget of self-consistency methods limits their application in high-computational-cost tasks. The core problem is how to reduce the sampling budget without sacrificing accuracy.
Innovation
Adaptive-Consistency introduces a dynamic sampling strategy based on a Dirichlet distribution, breaking the fixed budget limitation of traditional self-consistency methods. It uses a lightweight stopping criterion to dynamically adjust the number of samples per question, improving sampling efficiency.
Methodology
- �� Model sample probability distribution using Dirichlet distribution
- �� Design a lightweight stopping criterion to dynamically adjust sampling
- �� Conduct experiments on various datasets and models
- �� Provide code and data for reproducibility
Experiments
Experiments were conducted on 17 datasets and 3 large language models, including VICUNA-13B, CODE-DAVINCI-002, and GPT-3.5-TURBO. Different sampling strategies and stopping criteria were compared to evaluate sampling efficiency and output accuracy.
Results
Experimental results show that Adaptive-Consistency performs excellently across various tasks and models, reducing the number of samples by up to 7.9 times with an accuracy drop of less than 0.1%.
Applications
This method is applicable to large language model applications requiring efficient sampling, such as code generation and complex reasoning tasks. Its flexible sampling strategy allows for improved output quality without increasing computational costs.
Limitations & Outlook
While Adaptive-Consistency excels in reducing sampling budgets, the computation of the stopping criterion may require additional resources in complex tasks. Future research can explore more efficient stopping criteria and more complex models to further enhance the method's performance.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional methods are like making 40 dishes each time and then choosing the most popular one. Adaptive-Consistency is like tasting the dish as you cook, and once it's good enough, you stop making more. This saves time and ingredients while ensuring quality.
ELI14 Explained like you're 14
Imagine you're playing a game where you have to choose the best strategy. Traditional methods are like trying 40 strategies each time and then picking the best one. Adaptive-Consistency is like trying a few strategies and once you find a really good one, you stop trying others. This method saves time and ensures you perform well in the game.
Glossary
Self-Consistency
A method that improves model accuracy by generating multiple samples and selecting the most frequent output.
Used to enhance the correctness of large language model outputs.
Dirichlet Distribution
A probability distribution used to model parameters of a multinomial distribution.
Used in Adaptive-Consistency to model sample probability distribution.
Stopping Criterion
A rule that determines when to stop sampling.
Used in Adaptive-Consistency to dynamically adjust the number of samples.
VICUNA-13B
A fine-tuned large language model.
Used as an evaluation model in experiments.
Code Generation
The task of generating executable code from a textual description.
Used as a task category in experiments.
Open Questions Unanswered questions from this research
- 1 How can the stopping criterion be optimized further without increasing computational resources?
- 2 How can Adaptive-Consistency's stability be improved in tasks with high uncertainty?
Applications
Immediate Applications
Code Generation Optimization
By reducing the number of samples, improve the efficiency and accuracy of code generation tasks.
Long-term Vision
General Large Language Model Optimization
Apply Adaptive-Consistency to more tasks, reducing computational costs and improving output quality.
Abstract
A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution. Existing Self-Consistency techniques always generate a constant number of samples per question, where a better approach will be to non-uniformly distribute the available budget based on the amount of agreement in the samples generated so far. In response, we introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion. Our experiments over 17 reasoning and code generation datasets and three LLMs demonstrate that Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%. Our code and data are available at https://www.sample-step-by-step.info