Universal Self-Adaptive Prompting (USP)

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Introduction

Modern Large Language Models (LLMs) possess impressive zero-shot abilities making them ideal for numerous applications like classification, text generation, etc. This versatility has driven widespread adoption. However, zero-shot prompting often suffers from inconsistent or suboptimal performance due to the lack of clear directions. This variability can lead to unreliable results for the same query.

Few-shot prompting—providing examples alongside the query—can significantly improve performance but requires labeled data, which is expensive and time-consuming to obtain. This challenge becomes more pronounced as LLMs are applied across diverse tasks, each requiring its own labeled examples.

Methods like Self-Consistency (SC) and Consistency-based Self-Adaptive Prompting (COSP) have attempted to improve zero-shot performance but have notable limitations:

1. Self-Consistency (SC): Generates multiple few-shot responses and aggregates them—an approach that is computationally expensive and time-intensive.
2. Consistency-based Self-Adaptive Prompting (COSP): Task-specific and lacks versatility, making it less applicable across diverse scenarios.

Introducing Universal Self-Adaptive Prompting (USP)

Universal Self-Adaptive Prompting (USP) is an innovative prompt design method developed for zero-shot learning in large language models (LLMs). Zero-shot learning involves prompting a model to perform a task without using any labeled examples for guidance. USP was created to help LLMs perform consistently well across diverse tasks without requiring human-generated examples for tuning. Instead, it generates pseudo-demonstrations—examples made from model predictions that guide the model to produce better answers on subsequent prompts.

Key USP Components:

1. Pseudo-Demonstrations: USP leverages model predictions to create examples that resemble a few-shot learning context, thereby improving model accuracy without labeled data.
2. Task-Type Adaptation: USP identifies the type of task—such as classification (CLS), short-form generation (SFG), or long-form generation (LFG) and applies specific scoring functions to adapt prompts effectively.
3. Scoring and Selection: For each task type, USP uses a selector that scores pseudo-demonstrations based on model confidence, choosing only the most reliable ones to improve prompt quality.

Why USP Stands Out?

Unlike traditional prompting techniques, USP is:

• Fully Zero-Shot: Operates without labeled data, making it cost-effective and scalable.
• Flexible: Adapts to diverse task types, including classification, reasoning, and text generation.
• Performance-Driven: Achieves results comparable to or better than few-shot prompting, even in complex tasks.

How to Use Universal Self-Adaptive Prompting (USP)?

USP selects task-specific prompts and uses model-generated examples as in-context “demos”, essentially guiding the model as if it were in a few-shot setting. Here's how it works for each task type:

1. Classification (CLS) Tasks

• Example Task: Sentiment analysis.
• Scoring: USP uses entropy of class probabilities to gauge confidence and ensures coverage across all possible classes.
• Template: Selects pseudo-demos with the highest confidence, balancing class representation.

2. Short-Form Generation (SFG) Tasks

• Example Task: Fact-based question answering.
• Scoring: USP evaluates self-consistency by comparing model responses generated multiple times, selecting the most consistent answers.
• Template: USP compiles the most frequent and confident responses as demos.

3. Long-Form Generation (LFG) Tasks

• Example Task: Summarization.
• Scoring: USP measures consistency in outputs using similarity metrics (e.g., ROUGE scores) across repeated responses.
• Template: Identifies pseudo-demos by selecting the most consistent long-form responses, adjusted for diversity.

Algorithm Overview

1. Select a subset of test queries to generate initial pseudo-demos.
2. For each query:

• Generate responses for classification tasks or multiple outputs for generation tasks using a non-zero temperature setting.
• Add candidates to the pseudo-demo pool.

3. Score candidates and build a final pseudo-demo set.
4. Use the refined pseudo-demos to create a few-shot-like prompt and query the LLM for the final response.

Results of the Universal Self-Adaptive Prompting (USP)

In testing with various models (e.g., PaLM-540B, PaLM 2), USP often outperformed standard zero-shot methods and, in many cases, approached or even surpassed few-shot baselines across over 40 tasks.

These results highlight USP's capacity to significantly improve zero-shot accuracy by generating more effective prompts.

What Are the Limitations of Universal Self-Adaptive Prompting (USP)?

Despite its strengths, USP has a few limitations:

• Focus on In-Context Learning: It optimizes in-context examples but does not refine other input components.
• Dependence on Model Capabilities: USP requires models with strong in-context learning abilities and well-calibrated uncertainty outputs, favoring larger, more capable models.
• Generative Task Variability: While USP improves zero-shot performance for generative tasks, it may not always surpass few-shot prompting.
• Limited Evaluation on Non-Text Outputs: USP has not been extensively tested on tasks requiring non-text outputs, such as code generation.

Conclusion

Universal Self-Adaptive Prompting (USP) offers a groundbreaking solution for zero-shot learning in LLMs, bridging the gap between performance and scalability by leveraging pseudo-demonstrations. Its ability to adapt to diverse tasks without requiring labeled data makes it a versatile and cost-effective approach, paving the way for more efficient and accessible AI applications.