Dynamic Prompt Tuning for Language Model Efficiency

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Solution Overview

Problem

Fine-tuning large language models for specific tasks is inefficient due to their vast number of parameters, making it resource-intensive and less effective.

Innovation Solution

The method involves dynamic prompt tuning, where the position, length, and pool of prompts are adapted based on the language processing task, allowing for a more efficient and effective interaction with the language model by generating a combined input that optimizes the prompt placement and length for superior task-specific results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If fine-tuning is applied to the entire pretrained language model, then task-specific performance is improved, but computational resources and time are excessively consumed

Engineering Contradiction:

Improvetask-specific performanceVSAvoidfine-tuning efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The invention segments the fine-tuning process by separating the static pretrained model parameters from the dynamic prompt parameters. Only the prompt parameters (a small subset) are fine-tuned instead of the entire model, dividing the optimization task into manageable parts that reduce computational burden while maintaining task-specific performance.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The invention extracts and isolates the prompt parameters from the full model parameters for dedicated optimization. By taking out only the necessary prompt components for fine-tuning rather than the entire model, the method achieves task adaptation with significantly reduced computational resources.

Inventive Principle:

Principle #2Taking out (Extraction)

2Reliability

If fixed prompts are used for language model input, then implementation is simple, but task-specific performance is suboptimal

Engineering Contradiction:

Improvetask-specific performanceVSAvoidprompt tuning complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The invention transforms static fixed prompts into dynamic task-adaptive prompts. The prompt parameters are made learnable and can be dynamically optimized based on specific tasks, allowing the system to adapt prompt content, length, and structure to match task requirements while maintaining manageable complexity through parameter sharing.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The invention changes the parameters of prompts (content, length, structure) from fixed to learnable variables. By optimizing prompt parameters specifically for each task rather than using universal fixed prompts, the system achieves superior task-specific performance with controlled complexity through the parameter-efficient approach.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240304329A1Dynamic prompt tuning of machine learning model inputs

Publication Date: 2024.09.12 NEC LABORATORIES AMERICA INC

AI summary

Methods and systems for prompt tuning include training a tuning function to set prompt position, prompt length, or prompt pool based on a language processing task. The tuning function is applied to an input query to generate a combined input, with prompt text having the prompt length, being selected according to the prompt pool, and being added to the input query at the prompt position. The combined input is applied to a language model.