Optimising Hard Prompts with Few-Shot Meta-Prompting (2407.18920v1)

Abstract: Prompting is a flexible and adaptable way of providing instructions to a LLM. Contextual prompts include context in the form of a document or dialogue along with the natural language instructions to the LLM, often constraining the LLM to restrict facts to that of the given context while complying with the instructions. Masking the context, it acts as template for prompts. In this paper, we present an iterative method to generate better templates using an LLM from an existing set of prompt templates without revealing the context to the LLM. Multiple methods of optimising prompts using the LLM itself are explored to check the effect of few shot sampling methods on iterative propagation while maintaining linguistic styles and syntax on optimisation of prompt templates, yielding a 103.87% improvement using the best performing method. Comparison of the results of multiple contextual tasks demonstrate the ability of LLMs to maintain syntax while learning to replicate linguistic styles. Additionally, the effect on the output with different methods of prompt template generation is shown.

Essay on "Optimising Hard Prompts with Few-Shot Meta-Prompting"

The paper "Optimising Hard Prompts with Few-Shot Meta-Prompting" by Sayash Raaj Hiraou investigates methodologies for optimizing hard prompts using LLMs. Hard prompts, distinct from soft prompts that rely on vector fine-tuning, involve explicit and interpretable natural language instructions. They are crucial in effectively guiding LLMs to generate relevant outputs across tasks such as question answering, summarization, and dialogue summarization. The paper provides an empirical analysis of optimizing these hard prompt templates without altering the underlying context, focusing on maintaining linguistic integrity and style.

Methodology

The paper describes an iterative method for generating better prompt templates using LLMs themselves. The method involves leveraging few-shot meta-prompting, where LLMs are tasked with creating optimized prompts based on a set of initial templates. Two main components form the experimental setup: the feeder method and the propagation method.

1. Feeder Methods:
   • Feeder Method A samples the top-performing prompts based on evaluation scores, aiming for an optimally refined result through progressive iterations.
   • Feeder Method B incorporates both top and bottom performing prompts to diversify the generation process by introducing negative examples to the LLM.
2. Propagation Methods:
   • Propagation Method A concatenates all previously generated prompts before each iteration, risking context overflow but potentially enriching the prompt set.
   • Propagation Method B relies on sample selection at each step, balancing between retaining previously learned knowledge and introducing new variation.

The combination of these methods results in four possible configurations tested across multiple contextual tasks. Each configuration is evaluated based on mean ROUGE-L F1 scores, maximum prompt efficiency, and similarity scores to assess diversity and overfitting risks.

Results

The findings illustrate that the propagation method generally plays a more significant role in optimizing prompt templates than the choice of feeder method. Specifically, the combination C—using Feeder Method A and Propagation Method B—demonstrated the most promising balance of prompt diversity and optimization efficiency, surpassing the initial set of manually created prompts. Notably, the optimized templates yielded a 103.87% improvement in specific tasks, marking a significant enhancement in prompt performance.

Implications and Future Directions

These findings highlight the capacity of LLMs to serve not only as effective task solvers but also as meta-optimizers of their instructional inputs. The ability to refine prompts using inherent LLM mechanisms underscores a potential shift towards more autonomous NLP systems, where LLMs can partially self-regulate their operational parameters through meta-prompting.

For future research, further exploration into the dynamics between varying feeder and propagation methods could yield insight into the broader applicacy across different LLM architectures. Moreover, extending experimentation across diverse datasets and tasks would contribute to a more generalized understanding of prompt optimization processes. Furthermore, addressing overfitting and maintaining prompt diversity will be critical as the field continues to seek improvements in LLM output quality without compromising linguistic richness.

Conclusion

The paper presents a compelling paper into the optimization of hard prompts, showcasing the efficacy of LLMs as meta-prompt designers. The examination of few-shot prompting methodologies opens up avenues for refined prompt optimization, crucial for enhancing the interpretability and accuracy of LLM-generated outputs. Such research bridges a significant gap in NLP regarding the unexplored avenues of hard prompt tuning, setting the stage for more nuanced prompt engineering techniques.