Boosted Prompt Ensembles for Large Language Models

Original Paper: https://arxiv.org/abs/2304.05970

By: Silviu Pitis, Michael R. Zhang, Andrew Wang, Jimmy Ba

Abstract:

Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training. To further improve performance, we propose a prompt ensembling method for large language models, which uses a small dataset to construct a set of few shot prompts that together comprise a ``boosted prompt ensemble''. The few shot examples for each prompt are chosen in a stepwise fashion to be ``hard'' examples on which the previous step's ensemble is uncertain. We show that this outperforms single-prompt output-space ensembles and bagged prompt-space ensembles on the GSM8k and AQuA datasets, among others. We propose both train-time and test-time versions of boosted prompting that use different levels of available annotation and conduct a detailed empirical study of our algorithm.

Summary Notes

Boosted Prompt Ensembles for LLMs: Simplifying AI Reasoning

The landscape of artificial intelligence is continually evolving, with Large Language Models (LLMs) at the forefront of this transformation.

These models have shown exceptional skill in handling various tasks, especially in scenarios where they use given examples to navigate through problems.

However, the conventional method of manually creating prompts for these models is both time-consuming and a bottleneck for scalability.

Enter boosted prompting, a novel approach that enhances LLM performance through an ensemble of prompts, offering a more automated and efficient way to optimize these models.

The Challenge with Manual Prompts

LLMs are powerful but to unlock their full potential on complex tasks, they often need fine-tuning through a process called prompt engineering.

This requires creating specific examples to guide the model, a task that’s not only intricate but also demands a deep understanding of the model's mechanics.

As the use of LLMs grows across various industries, the demand for a scalable solution to prompt optimization is becoming more critical.

The Boosted Prompting Solution

Boosted prompting offers a streamlined alternative by automating prompt optimization. This method incrementally builds an ensemble of prompts that improve the model's reasoning on difficult examples. The process involves:

• Starting with an Initial Prompt: Establishing a performance baseline with a few-shot prompt.
• Identifying Difficult Examples: Pinpointing where the model struggles.
• Creating a Boosted Prompt Ensemble: Adding new, tailored prompts to the ensemble to address these challenges.

Experimental Insights

Our research, using complex datasets like GSM8K and AQua, showcases the effectiveness of boosted prompting.

We experimented with different factors, such as the number of prompts and the complexity of reasoning paths, and found that this approach significantly outperforms traditional methods in enhancing LLM reasoning capabilities.

Key Advantages

• Efficiency: Boosted prompting leverages the model's ability to generate its own training data, streamlining the refinement of its reasoning.
• Flexibility: It adapts in real-time, making it capable of handling shifts in problem types – a crucial feature for practical applications.
• Considerations: The approach's success largely depends on the quality of the initial prompts and the model's ability to create effective reasoning paths.

Looking Forward

Boosted Prompt Ensembles present an exciting development in maximizing the potential of LLMs. This method simplifies prompt optimization, improving reasoning performance without extra training. Its adaptability and efficiency make it especially appealing for businesses in need of scalable AI solutions.

For AI engineers, incorporating boosted prompting could significantly enhance the capability and efficiency of LLM applications, from complex reasoning tasks to more general uses.

This approach not only represents a step forward in practical AI application but also marks a shift towards more autonomous and scalable model optimization.

Dive Deeper

To explore the technical depths of boosted prompting, the original research paper and related literature on LLMs, prompting techniques, and ensemble methods offer extensive insights. These resources are essential for those looking to understand the methodology, experiment with the technique, or explore its theoretical foundations.

Appendices

For a detailed look at implementation strategies, further experimental results, and the potential of applying boosted prompting in online settings, the appendices of the original research paper are invaluable.

They provide a comprehensive guide for both practitioners and researchers interested in the practical and theoretical aspects of this innovative technique.