Task Facet Learning: A Structured Approach to Prompt Optimization (2406.10504v1)

Abstract: Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a LLM. Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We identify and exploit structure in the prompt optimization problem -- first, we find that prompts can be broken down into loosely coupled semantic sections that have a relatively independent effect on the prompt's performance; second, we cluster the input space and use clustered batches so that the optimization procedure can learn the different facets of a task across batches. The resulting algorithm, UniPrompt, consists of a generative model to generate initial candidates for each prompt section; and a feedback mechanism that aggregates suggested edits from multiple mini-batches into a conceptual description for the section. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate. Code for UniPrompt will be available at \url{https://aka.ms/uniprompt}.

• The paper introduces UniPrompt, a generative algorithm that optimizes prompt synthesis by integrating diverse task facets.
• It employs semantic section structuring and clustering to organize prompt components and tailor them for large language models.
• Empirical results demonstrate that UniPrompt outperforms human-designed prompts and state-of-the-art methods, enhancing LLM accuracy.

"Task Facet Learning: A Structured Approach to Prompt Optimization" introduces a novel method for optimizing prompts for LLMs. The primary challenge addressed by this work is the synthesis of task information—comprising a basic description and training examples—into effective text prompts. Unique among existing methods, this paper proposes leveraging multiple facets of a task to inform prompt construction.

Key contributions and insights from the paper include:

1. Multiple Facets of Task Understanding:
   • The research emphasizes the human approach to prompt creation, which accounts for various facets such as counter-examples, explanations, and analogies.
   • The recognition that current algorithmic methods, which either iteratively edit prompts or automatically select in-context examples, fall short in encompassing these diverse facets.
2. Semantic Section Structuring:
   • The authors identify that prompts can be divided into loosely coupled semantic sections, each having an independent influence on the prompt's performance.
   • This crucial observation allows the prompt optimization problem to be tackled in a more structured manner.
3. Clustering Input Space:
   • Input data is clustered, and batches are formed from these clusters to facilitate learning distinct facets of the task across different clusters.
4. UniPrompt Algorithm:
   • Generative Model for Prompt Sections: The model generates initial candidate texts for each section of the prompt, facilitating the inclusion of the various facets.
   • Feedback Mechanism: This mechanism consolidates suggestions from different mini-batches into a coherent conceptual description for each section.
   • Empirical Performance: UniPrompt was evaluated on multiple datasets and a real-world task, showing superior performance over human-designed prompts and state-of-the-art methods. Notably, UniPrompt excels at generating long and complex prompts which are beyond the capabilities of current methods.

The paper presents UniPrompt, a novel algorithm that significantly enhances prompt optimization for LLMs by structurally integrating multiple task facets. This approach not only bridges gaps left by previous methods but also establishes a stronger framework for prompt synthesis, leading to higher accuracy and more effective LLM applications. The authors have also committed to making the code for UniPrompt publicly available, which may facilitate further advancements and practical applications in the field.