Automatic Prompt Selection for Large Language Models

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Introduction

Large Language Models (LLMs), renowned for handling a myriad of natural language processing tasks, often require expertly crafted prompts to optimize their performance for specific tasks. Creating these optimal prompts, however, is both labor-intensive and time-consuming. The research paper "Automatic Prompt Selection for Large Language Models" introduces a new, efficient method for automatically selecting the best prompts for any given input.

Introduction

LLMs can handle various tasks but require the best prompts to optimize their performance. Current methods to improve prompts either lack flexibility or efficiency. This paper proposes an effective method to automate the selection of the best prompt for any input, striking a balance between general and specific prompts while avoiding resource-heavy training and testing.

Key Steps

1. Group Training Data into Clusters: Generate candidate prompts for each group using LLM-based prompt generation.
2. Create a Data Set for Training a Prompt Evaluator: Train the evaluator to rank prompts based on their relevance to the input.
3. Use the Evaluator to Select the Best Prompt During Testing: This efficient method performs well on zero-shot question-answering datasets, such as JSM, HK, multi-RF, and AQA, showing competitive results and proving its effectiveness.

Related Work

Prompt Engineering

Prompt engineering involves manual or automatic strategies to optimize LLM performance across tasks. It includes:

• Prompt Tuning: A gradient-based approach to refine prompts, but it has limitations, such as requiring access to LLM parameters.
• Prompt Generation: Creating prompt tokens using optimization techniques like reinforcement learning and evaluation algorithms.
• Prompt Selection: Identifying high-quality prompts tailored to specific tasks and inputs, but it often incurs high computational costs and latency.

Problem Statement

Finding an optimal prompt generator (D) for each question (Q) and context (C) guides the LLM (M) in producing the correct output (A). Challenges include extensive prompt search space and cost-prohibitive processes due to multiple iterations of querying LLMs.

Proposed Solution

The Prompt Evaluator

Instead of a generative model, a prompt evaluator scores the fitness of a prompt (P) for a given (Q) and (C). This reduces computational costs and enhances efficiency. The process includes:

1. Prompt Database Generation: Create a fixed database of representative prompts.
2. Prompt Evaluator Training: Train an evaluator to assign scores indicating prompt effectiveness for given inputs.
3. Prompt Ranking: Rank prompts from the database and select the highest-scoring prompt.

Steps in Detail

Prompt Database Generation

1. Clustering: Assign training data into clusters so that similar inputs share the same prompt.
   • Encoding: Use a sentence transformer to encode concatenated question and context pairs.
   • Clustering Algorithm: Apply K-means clustering on encoded representations.
2. Meta Prompt Generation:
   • Generate prompts for each cluster using a generative approach.
   • Use an LLM to create candidate prompts and remove duplicates to ensure a unique database.

Prompt Evaluator Training

1. Data Collection: Prepare a comparison dataset using preference learning to distinguish good and bad prompts.
   • Group related prompts into good or bad based on their performance.
2. Evaluator Training: Train the evaluator to differentiate between good and bad prompts using a loss function.

Prompt Ranking

1. Score Calculation: Calculate relevance scores for new inputs using the evaluator.
2. Prompt Selection: Select the top-k scored prompts and apply a voting mechanism to determine the most accurate output.

Experimental Setup

The researchers used datasets like JSM, HK, multi-RF, and AQA. Models and configurations included:

• Prompt Generator: GPT-3.5 Turbo
• Training Setup for Prompt Evaluator: Optimizer Adam, weight decay 0.1, batch size 16, epochs 30.
• Clustering: 10 clusters, 3 prompts per cluster.
• Meta Prompt Generation: 10 demonstrations per meta prompt.
• Training Costs: Approximately $ 40 USD in total.

Case Study

A sample problem from the AQA dataset demonstrates the effectiveness of the method. The good prompt produced the correct answer ('E') with a high relevance score, while the bad prompt yielded no useful answer.

Results and Discussion

• Accuracy: The automatic prompt selection, particularly with top-k selection and voting, achieved high accuracy across datasets.
• Efficiency: The approach provided a balance between specificity and efficiency, outperforming manual crafting of prompts.

Keywords

• Large Language Models (LLMs)
• Prompt Engineering
• Prompt Tuning
• Prompt Generation
• Prompt Selection
• Clustering
• Meta Prompt Generation
• Prompt Evaluator

FAQ

Q1: What are the key steps in the automatic prompt selection method?

A1: The key steps are grouping training data into clusters and generating candidate prompts, creating a dataset for training a prompt evaluator, and using the evaluator to select the best prompt during testing.

Q2: How does prompt evaluator training work?

A2: It involves preparing a comparison dataset to distinguish good and bad prompts and training the evaluator to assign relevance scores to prompts based on their effectiveness.

Q3: What datasets were used to test this method?

A3: The datasets include JSM, HK, multi-RF, and AQA, each with specific characteristics and complexity levels.

Q4: What models and configurations were used in the experiments?

A4: GPT-3.5 Turbo was used for prompt generation, and the Adam optimizer with specific settings was used for training the prompt evaluator.

Q5: How does the method compare to manually crafted prompts?

A5: The method provides more creative and diverse prompts, automates the generation process, and significantly reduces reliance on human-created prompts.