Reinforcement Learning Prompt Template Optimization for Small Sample Fine-Tuning
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Solution Overview
Problem
The instability of small sample learning in fine-tuning pre-trained language models for downstream tasks due to variations in prompt template formats, training samples, and order, leading to accuracy fluctuations and the need for laborious manual design of prompt templates, which can result in over-fitting with limited labeled data.
Innovation Solution
A method using reinforcement learning to construct and optimize prompt templates and tag words by forming input samples, calculating cosine similarity, and reducing the search space through near-synonym sets and conditional probabilities, thereby improving accuracy and reducing system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual design of prompt templates is used, then task accuracy can be optimized, but the process becomes laborious and time-consuming
Solution Approach 1:
The system enables automatic prompt template generation through reinforcement learning, where the model learns to construct effective prompts independently without human intervention. The reinforcement learning agent automatically searches the prompt space and selects optimal templates based on task performance feedback.
Solution Approach 2:
Reinforcement learning serves as an intermediary between the task requirements and prompt template generation. The RL agent mediates the search process by learning policies that map task characteristics to effective prompt structures, bridging the gap between manual design quality and automated efficiency.
2Measurement precision
If reinforcement learning is used to search for optimal prompt templates, then task accuracy improves, but the search space is large and may cause over-fitting with limited data
Solution Approach 1:
The system performs partial search by focusing on the most promising regions of the prompt space rather than exhaustively searching all possibilities. Reinforcement learning guides the search toward high-reward areas, performing enough exploration to find effective templates without exhaustive coverage that would lead to over-fitting.
Solution Approach 2:
The reinforcement learning framework incorporates feedback loops where task performance on validation data continuously guides the prompt search. The reward signal from task accuracy feedback prevents over-fitting by ensuring prompts generalize well to unseen data, not just memorizing training examples.
3Quantity of substance
If prompt-based fine-tuning is used, then no new parameters are needed and memory usage is reduced, but the gap between pre-training and downstream tasks remains significant
Solution Approach 1:
The system changes the approach from modifying model parameters to modifying prompt parameters. By treating prompt templates as the adjustable element rather than model weights, the system adapts to downstream tasks while keeping the pre-trained model parameters fixed, thus reducing memory usage while maintaining adaptability through learned prompt structures.
Data Source
AI summary
The present application relates to the technical field of computers. Provided is a small sample fine-turning method, the method comprising: inputting a data set, and forming an input sample according to a fixed template; constructing a candidate tag word set and a candidate prompt template set; by means of reinforcement learning, searching an optimal tag word corresponding to the input sample from the candidate tag word set, and a prompt template corresponding to the input sample from the candidate prompt template set; and outputting a mapping relationship of the optimal tag word and an optimal prompt template format corresponding to the prompt template.