Beginner Explanation
Imagine you’re learning to ride a bike. Sometimes, your friend might say, ‘Try leaning a little to the right when you turn.’ This is a suggestion that helps you improve your riding skills. Improvement prompting strategies are like those friendly tips for people working with AI. They help users understand how to make their work better by giving them hints based on what they’re currently doing well or not so well. So, just as you become a better biker with advice, people using AI can become better at their tasks with these helpful suggestions.Technical Explanation
Improvement prompting strategies in machine learning involve techniques that analyze the performance of operators (e.g., algorithms, models) and provide actionable feedback to enhance their effectiveness. These strategies may include reinforcement learning, where a model learns optimal actions through rewards, or supervised learning, where feedback is given based on labeled data. For example, consider a text generation model: if it produces incoherent sentences, a prompting strategy could involve providing examples of well-structured sentences to guide its learning. In Python, this could look like: “`python from transformers import GPT2LMHeadModel, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained(‘gpt2’) model = GPT2LMHeadModel.from_pretrained(‘gpt2′) input_text = “The sun is shining brightly” inputs = tokenizer.encode(input_text, return_tensors=’pt’) outputs = model.generate(inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) “` Here, the model is prompted with a coherent sentence to improve its output quality.Academic Context
Improvement prompting strategies are rooted in the fields of human-computer interaction and adaptive learning systems. They draw on theories from cognitive psychology, such as feedback loops and scaffolding, which emphasize the importance of guidance in learning. Key papers include ‘Adaptive Learning: A Review’ by K. M. R. K. Rajan et al., which discusses how adaptive systems can enhance learning outcomes, and ‘Feedback in Learning: The Role of Prompting’ by Hattie and Timperley, which explores the impact of feedback on performance. Mathematically, these strategies can be modeled using Bayesian optimization techniques, where the goal is to maximize a utility function based on prior performance data.Code Examples
Example 1:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
input_text = "The sun is shining brightly"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Example 2:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
View Source: https://arxiv.org/abs/2511.16485v1